incrementalPCA

Incremental principal component analysis

Since R2024a

Description

The incrementalPCA function creates an incrementalPCA model object that is suitable for incremental principal component analysis (PCA). Unlike the pca function, for which you must provide all of the data before computing the principal component coefficients, incrementalPCA allows you to update the coefficients incrementally by supplying chunks of data to the incremental fit function.

Unlike other Statistics and Machine Learning Toolbox™ model objects, incrementalPCA can be called directly. Also, you can specify incremental PCA options, such as the estimation period, warm-up period, variable weights, and whether to standardize the predictor data before fitting the model to data. After you create an incrementalPCA object, it is prepared for incremental PCA.

Creation

You can create an incrementalPCA model object in two ways:

Call the function directly — Configure incremental PCA options by calling incrementalPCA directly. This approach is best when you do not have data yet or you want to start incremental PCA immediately. When you call incrementalPCA, you can specify principal component coefficients and variances so that the initial model is warm.
Call the incremental fit function — fit accepts a configured incrementalPCA model object and data as input, and returns an incrementalPCA model object updated with information computed from the input model and data.

Syntax

IncrementalMdl = incrementalPCA

IncrementalMdl = incrementalPCA(Name=Value)

Description

IncrementalMdl = incrementalPCA returns a default incremental PCA model object IncrementalMdl. Properties of a default model contain placeholders for unknown model parameters.

IncrementalMdl = incrementalPCA(Name=Value) sets properties and additional options using name-value arguments. For example, incrementalPCA(StandardizeData=true,EstimationPeriod=1000) specifies to standardize the predictor data using a hyperparameter estimation period of 1000 observations.

example

Input Arguments

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Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: IncrementalMdl = incrementalPCA(StandardizeData=true,CenterData=true) specifies to standardize and center the values of each variable.

`CenterData` — Flag to center data
`false` or `0` (default) | `true` or `1`

Flag to center the data, specified as logical 0 (false) or 1 (true). If CenterData=true, the incremental fit function estimates the predictor means Mu during the estimation period specified by EstimationPeriod and subtracts them from the data before computing singular value decomposition or eigenvalue decomposition.

If you specify CenterData=true, the number of degrees of freedom is NumTrainingObservations – 1. Otherwise, the number of degrees of freedom is NumTrainingObservations.

If you specify StandardizeData=true, the software sets CenterData=true.

You cannot specify CenterData if you specify Coefficients and Latent.

Example: CenterData=true

Data Types: logical

`Means` — Column means
numeric row vector

Column means, specified as a numeric row vector.

If you specify Means:

You must also specify Coefficients, Latent, and NumObservations.
The Mu property of IncrementalMdl is updated with the values in Means.

Example: Means=[0 0 0.5 0.5 0.5]

Data Types: single | double

`NumObservations` — Number of observations
positive integer

Number of observations, specified as a positive integer.

If you specify NumObservations, you must also specify Coefficients and Latent.

If you also specify Means, the number of degrees of freedom is NumObservations + NumTrainingObservations – 1. Otherwise, the number of degrees of freedom is NumObservations + NumTrainingObservations.

Example: NumObservations=100

Data Types: single | double

`StandardizeData` — Flag to standardize predictor data
`false` or `0` (default) | `true` or `1`

Flag to standardize the predictor data, specified as logical 0 (false) or 1 (true). If StandardizeData=true, the incremental fit function estimates the predictor means Mu and standard deviations Sigma during the estimation period specified by EstimationPeriod, and standardizes the predictor data.

If you specify StandardizeData=true, the number of degrees of freedom is NumTrainingObservations - 1. Otherwise, the number of degrees of freedom is NumTrainingObservations.

You cannot specify StandardizeData if you specify Coefficients and Latent.

Example: StandardizeData=true

Data Types: logical

Properties

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You can set most properties by using name-value argument syntax when you call incrementalPCA directly. You cannot set the properties ExplainedVariance, IsWarm, Mu, NumTrainingObservations, and Sigma.

`Coefficients` — Principal component coefficients
numeric matrix

This property is read-only.

Principal component coefficients, specified as a p-by-n numeric matrix, where p is equal to NumPredictors and n is equal to NumComponents. If you specify Coefficients as a p-by-n numeric matrix when creating the model object, incrementalPCA sets NumPredictors=p and NumComponents=n. Each column of Coefficients contains coefficients for one principal component.

The incremental fit function updates Coefficients and reorders the columns in descending order by the principal component variances (see Latent). If NumTrainingObservations < NumComponents, the rightmost NumComponents - NumTrainingObservations columns of Coefficients are 0.

When you specify Coefficients:

You must also specify Latent.
If you also specify Latent, and VariableWeights, then Coefficients'*diag(VariableWeights)*Coefficients must be orthonormal.
If you do not specify Means and NumObservations, then Coefficients must be a square matrix.

If you do not specify Coefficients and Latent, then Coefficients is equal to zeros(NumPredictors,NumComponents) by default.

Example: Coefficients=0.1*eye(5)

Example: Mdl=incrementalPCA(Coefficients=coeff,Latent=latent,Means=mu,NumObservations=1000) creates an incremental PCA object Mdl using the outputs returned by [coeff,~,latent,~,~,mu]=pca(X).

Example: Mdl=incrementalPCA(Coefficients=coeff,Latent=latent) creates an incremental PCA object Mdl using the outputs returned by [coeff,latent]=pcacov(V).

Data Types: single | double

`Latent` — Principal component variances
numeric column vector

This property is read-only.

Principal component variances, namely the eigenvalues of the covariance matrix of the predictor data, specified as a numeric column vector. The software sets the length of Latent to NumComponents.

If you specify Latent:

You must also specify Coefficients.
The elements of Latent must have non-increasing values.
If you do not specify Means and NumObservations, the length of Latent must equal the number of rows in Coefficients.

The incremental fit function updates Latent and reorders the columns in descending order by the principal component variances. If NumTrainingObservations < NumComponents, the rightmost NumComponents - NumTrainingObservations columns of Latent are 0.

If you do not specify Latent and Coefficients when you create IncrementalMdl, then Latent is equal to zeros(NumComponents,1) by default.

Example: Latent=0.5*ones(5,1)

Example: Mdl=incrementalPCA(Coefficients=coeff,Latent=latent) creates an incremental PCA object Mdl using the outputs returned by [coeff,latent]=pcacov(V).

Data Types: single | double

`EstimationPeriod` — Number of observations processed to estimate hyperparameters
nonnegative integer

This property is read-only.

Number of observations processed by the incremental model to estimate hyperparameters (Mu and Sigma), specified as a nonnegative integer.

If you specify a positive EstimationPeriod, and both StandardizeData and CenterData are false, incrementalPCA sets EstimationPeriod to 0.
If IncrementalMdl is prepared for incremental PCA (all hyperparameters required for training are specified), incrementalPCA sets EstimationPeriod to 0.
If IncrementalMdl is not prepared for incremental PCA, and either StandardizeData or CenterData is true, incrementalPCA sets EstimationPeriod to 1000 and estimates the unknown hyperparameters.
When processing observations during the estimation period, the software ignores observations that contain at least one missing value.

For more details, see Estimation Period.

Data Types: single | double

`ExplainedVariance` — Percentage of total variance explained
numeric column vector

This property is read-only.

Percentage of the total variance explained by each principal component, specified as a numeric column vector. The columns are in descending order by the principal component variances. The length of ExplainedVariance equals NumComponents. The values of ExplainedVariance add up to 100% if NumPredictors and NumComponents are equal. If NumTrainingObservations < NumComponents, the last NumComponents - NumTrainingObservations elements of ExplainedVariance are 0.

You cannot specify ExplainedVariance directly.

Data Types: single | double

`IsWarm` — Flag indicating whether `fit` returns transformed data
`false` or `0` | `true` or `1`

This property is read-only.

Flag indicating whether the incremental fit function returns transformed data, specified as logical 0 (false) or 1 (true).

If IsWarm is false, the Xtransformed output of fit consists of NaN values.

The incremental model IncrementalMdl is warm (IsWarm is true) if you specify Coefficients and Latent when you create IncrementalMdl. Otherwise, IsWarm becomes true after the incremental fit function fits the incremental model to WarmupPeriod observations.

If EstimationPeriod > 0, then during the estimation period, fit does not fit the model and IsWarm is false.

You cannot specify IsWarm directly.

Data Types: logical

`Mu` — Predictor means
numeric vector | `[]`

This property is read-only.

Predictor means, specified as a numeric vector.

If Mu is an empty array [] and you specify CenterData=true or StandardizeData=true, then the incremental fit function sets Mu to the predictor variable means estimated during the estimation period specified by EstimationPeriod.

You cannot specify Mu directly. However, if you specify Means and NumObservations when you create IncrementalMdl, then incrementalPCA sets Mu to the values in Means.

Data Types: single | double

`NumComponents` — Number of principal components
nonnegative integer

This property is read-only.

Number of principal components to keep after fitting the model, specified as a nonnegative integer.

If you specify NumComponents:

You cannot specify Coefficients and Latent.
The default value of NumPredictors is 0.

If you specify VariableWeights and do not specify NumComponents, then incrementalPCA sets NumComponents to be equal to the length of VariableWeights.

If you specify Coefficients and Latent, incrementalPCA sets NumComponents to be equal to the number of rows of Coefficients and the length of Latent.

Example: NumComponents=3

Data Types: single | double

`NumPredictors` — Number of predictor variables
nonnegative integer

This property is read-only.

Number of predictor variables used to fit the model, specified as a nonnegative integer.

If you specify NumPredictors:

You cannot specify Coefficients and Latent.
The default value of NumComponents is NumPredictors.
NumComponents, if specified, must be less than or equal to NumPredictors.

If you specify VariableWeights, incrementalPCA sets NumPredictors to be equal to the length of VariableWeights.

If you specify Coefficients and Latent, incrementalPCA sets NumPredictors to be equal to the number of rows of Coefficients and the length of Latent.

Data Types: single | double

`NumTrainingObservations` — Number of observations fit to incremental model
nonnegative numeric scalar

This property is read-only.

Number of observations fit to the incremental model IncrementalMdl, specified as a nonnegative numeric scalar. NumTrainingObservations increases when you pass IncrementalMdl and training data to fit outside of the estimation period.

When fitting the model, the software ignores observations that contain at least one missing value.

You cannot specify NumTrainingObservations directly.

Data Types: double

`Sigma` — Predictor standard deviations
numeric vector | `[]`

This property is read-only.

Predictor standard deviations, specified as a numeric vector.

If Sigma is an empty array [] and you specify StandardizeData=true, the incremental fit function sets Sigma to the predictor variable standard deviations estimated during the estimation period specified by EstimationPeriod.

You cannot specify Sigma directly.

Data Types: single | double

`VariableWeights` — Variable weights
row vector of positive scalar values

This property is read-only.

Variable weights, specified as a row vector of positive scalar values.

If you specify VariableWeights, incrementalPCA sets NumPredictors and NumComponents to be equal to the length of VariableWeights.

If you specify VariableWeights, Coefficients, and Latent, then Coefficients'*diag(VariableWeights)*Coefficients must be orthonormal.

Example: VariableWeights=0.2*ones(5,1)

Data Types: single | double

`WarmupPeriod` — Number of observations to which model must be fit before it is warm
nonnegative integer

This property is read-only.

Number of observations to which the model must be fit before it is warm, meaning that the incremental fit function returns transformed data, specified as a nonnegative integer. When processing observations during the warm-up period, the software ignores observations that contain at least one missing value.

If WarmupPeriod < NumComponents, incrementalPCA sets WarmupPeriod to NumComponents.
If IncrementalMdl is prepared for incremental PCA (all hyperparameters required for training are specified), incrementalPCA sets WarmupPeriod to 0.
If IncrementalMdl is not prepared for incremental PCA and StandardizeData is true, incrementalPCA sets WarmupPeriod to 1000 and estimates the unknown hyperparameters.

Data Types: single | double

Object Functions

`fit`	Fit principal component analysis model to streaming data
`transform`	Transform data into principal component scores
`reset`	Reset incremental principal component analysis model

Examples

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Perform Incremental Principal Component Analysis Using Initial Model

Open Live Script

Perform principal component analysis (PCA) on an initial data chunk, and then create an incremental PCA model that incorporates the results of the analysis. Fit the incremental model to streaming data and analyze how the model evolves during training.

Load and Preprocess Data

Load the human activity data set.

load humanactivity

For details on the human activity data set, enter Description at the command line.

The data set includes observations containing 60 variables. To simulate streaming data, split the data set into an initial chunk of 1000 observations and a second chunk of 10,000 observations.

Xinitial = feat(1:1000,:);
Xstream = feat(1001:11000,:);

Perform Initial PCA

Perform PCA on the initial data chunk by using the pca function. Specify to center the data and keep 10 principal components. Return the principal component coefficients (coeff), principal component variances (latent), and estimated means of the variables (mu).

[coeff,~,latent,~,~,mu]=pca(Xinitial,Centered=true,NumComponents=10);

Create Incremental PCA Model

Create a model for incremental PCA that incorporates the PCA results from the initial data chunk.

IncrementalMdl = incrementalPCA(Coefficients=coeff,Latent=latent, ...
    Means=mu,NumObservations=1000);
details(IncrementalMdl)

  incrementalPCA with properties:

                     IsWarm: 1
    NumTrainingObservations: 0
               WarmupPeriod: 0
                         Mu: [0.7764 0.4931 -0.3407 0.1108 0.0707 0.0485 0.3931 -1.1100 0.0646 0.1703 -1.1020 0.0283 0.0836 -1.0797 0.0139 0.9328 1.2892 1.6731 2.0729 2.5181 2.9511 0.0128 0.0062 0.0039 0.0027 0.0020 0.0016 0.9322 ... ] (1x60 double)
                      Sigma: []
          ExplainedVariance: [10x1 double]
           EstimationPeriod: 0
                     Latent: [10x1 double]
               Coefficients: [60x10 double]
            VariableWeights: [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
              NumComponents: 10
              NumPredictors: 60

IncrementalMdl is an incrementalPCA model object. All its properties are read-only. Because Coefficients and Latent are specified, the model is warm, meaning that the fit function returns transformed observations.

Fit Incremental Model

Fit the incremental model IncrementalMdl to the data by using the fit function. To simulate a data stream, fit the model in chunks of 100 observations at a time. At each iteration:

Process 100 observations.
Overwrite the previous incremental model with a new one fitted to the incoming observations.
Store topEV, the explained variance value of the component with the highest variance, to see how it evolves during incremental fitting.

n = numel(Xstream(:,1));
numObsPerChunk = 100;
nchunk = floor(n/numObsPerChunk);
topEV = zeros(nchunk,1);

% Incremental fitting
for j = 1:nchunk
    ibegin = min(n,numObsPerChunk*(j-1) + 1);
    iend = min(n,numObsPerChunk*j);
    IncrementalMdl = fit(IncrementalMdl,Xstream(ibegin:iend,:));
    topEV(j) = IncrementalMdl.ExplainedVariance(1);
end

IncrementalMdl is an incrementalPCA model object fitted to all the data in the stream. The fit function fits the model to the data chunk and updates the model properties.

Analyze Incremental Model During Training

Plot the explained variance value of the component with the highest variance to see how it evolves during training.

figure
plot(topEV,".-")
ylabel("topEV")
xlabel("Iteration")
xlim([0 nchunk])

Figure contains an axes object. The axes object with xlabel Iteration, ylabel topEV contains an object of type line.

The highest explained variance value is 33% after the first iteration, and rapidly rises to 80% after five iterations. The value then gradually approaches 97%.

Perform Incremental Learning Incorporating Principal Component Analysis

Open Live Script

Create a model for incremental principal component analysis (PCA) and a default incremental linear SVM model for binary classification. Fit the incremental models to streaming data and analyze how the principal components, model parameters, and performance metrics evolve during training. Use the final models to predict activity labels.

Load and Preprocess Data

Load the human activity data set. Randomly shuffle the data.

load humanactivity
n = numel(actid);
rng(0,"twister") % For reproducibility
idx = randsample(n,n);
X = feat(idx,:);
Y = actid(idx);

For details on the human activity data set, enter Description at the command line.

Responses can be one of five classes: Sitting, Standing, Walking, Running, or Dancing. Dichotomize the response by identifying whether the subject is moving (actid > 2).

Y = Y > 2;

Specify the first 20,000 observations and labels as streaming data, and the remaining observations and labels as test data.

n = 20000;
Xstream = X(1:n,:);
Ystream = Y(1:n,:);
Xtest = X(n+1:end,:);
Ytest = Y(n+1:end,:);

Create Incremental Models

Create a model for incremental PCA. Specify to standardize the data, keep 3 principal components, and set a warm-up period of 2000 observations.

IncrementalPCA = incrementalPCA(StandardizeData=true, ...
    NumComponents=3,WarmupPeriod=2000);
details(IncrementalPCA)

  incrementalPCA with properties:

                     IsWarm: 0
    NumTrainingObservations: 0
               WarmupPeriod: 2000
                         Mu: []
                      Sigma: []
          ExplainedVariance: [3x1 double]
           EstimationPeriod: 1000
                     Latent: [3x1 double]
               Coefficients: [0x3 double]
            VariableWeights: [1x0 double]
              NumComponents: 3
              NumPredictors: 0

IncrementalPCA is an incrementalPCA model object. All its properties are read-only. By default, the software sets the hyperparameter estimation period to 1000 observations. The incremental PCA model must be warm (all hyperparameters are estimated) before the fit function returns transformed observations.

Create a default incremental linear SVM model for binary classification by using the incrementalClassificationLinear function.

IncrementalLinear = incrementalClassificationLinear;
details(IncrementalLinear)

  incrementalClassificationLinear with properties:

                    Learner: 'svm'
                     Solver: 'scale-invariant'
                  BatchSize: 1
                       Beta: [0x1 double]
                       Bias: 0
                    FitBias: 1
                 FittedLoss: 'hinge'
                     Lambda: NaN
                  LearnRate: 1
          LearnRateSchedule: 'constant'
                         Mu: []
                      Sigma: []
              SolverOptions: [1x1 struct]
           EstimationPeriod: 0
                 ClassNames: [0x1 double]
                      Prior: [1x0 double]
             ScoreTransform: 'none'
              NumPredictors: 0
    NumTrainingObservations: 0
        MetricsWarmupPeriod: 1000
          MetricsWindowSize: 200
                     IsWarm: 0
                    Metrics: [1x2 table]

IncrementalLinear is an incrementalClassificationLinear model object. All its properties are read-only. IncrementalLinear must be fit to data before you can use it to perform any other operations. By default, the software sets the metrics warm-up period to 1000 observations and the metrics window size to 200 observations.

Fit Incremental Models

Fit the IncrementalPCA and IncrementalLinear models to the streaming data by using the fit and updateMetricsAndFit functions, respectively. To simulate a data stream, fit each model in chunks of 50 observations at a time. At each iteration:

Process 50 observations.
Overwrite the previous incremental PCA model with a new one fitted to the incoming observations.
Return the transformed observations Xtr.
Overwrite the previous incremental classification model with a new one fitted to the incoming transformed observations.
Store $β_{1}$ , the cumulative metrics, and the window metrics to see how they evolve during incremental learning.
Store topEV, the explained variance of the component with the highest variance, to see how it evolves during incremental learning.

numObsPerChunk = 50;
nchunk = floor(n/numObsPerChunk);
ce = array2table(zeros(nchunk,2),"VariableNames",["Cumulative" "Window"]);
beta1 = zeros(nchunk,1);   
topEV = zeros(nchunk,1);

% Incremental learning
for j = 1:nchunk
    ibegin = min(n,numObsPerChunk*(j-1) + 1);
    iend = min(n,numObsPerChunk*j);
    [IncrementalPCA,Xtr] = fit(IncrementalPCA,Xstream(ibegin:iend,:));
    IncrementalLinear = updateMetricsAndFit(IncrementalLinear,Xtr, ...
        Ystream(ibegin:iend));
    beta1(j + 1) = IncrementalLinear.Beta(1);
    ce{j,:} = IncrementalLinear.Metrics{"ClassificationError",:};
    topEV(j + 1) = IncrementalPCA.ExplainedVariance(1);
end

During the incremental PCA estimation and warm-up periods, the fit function returns the transformed observations as NaNs. After the PCA estimation period and warm-up period, updateMetricsAndFit fits the linear coefficient estimates $β$ using the transformed observations. After the metrics warm-up period, IncrementalLinear is warm, and updateMetricsAndFit checks the performance of the model on the incoming transformed observations, and then fits the model to those observations.

Analyze Incremental Models During Training

To see how the highest explained variance, $β_{1}$ , and performance metrics evolve during training, plot them on separate tiles.

figure
t = tiledlayout(3,1);
nexttile
plot(topEV)
ylabel("Top EV [%]")
xline(IncrementalPCA.EstimationPeriod/numObsPerChunk,"r-.")
xlim([0 nchunk])
ylim([0 100])
nexttile
plot(beta1)
ylabel("\beta_1")
xline((IncrementalPCA.WarmupPeriod+ ...
    IncrementalPCA.EstimationPeriod)/numObsPerChunk,"b:")
xlim([0 nchunk])
nexttile
h = plot(ce.Variables);
xlim([0 nchunk])
ylabel("Classification Error")
xline((IncrementalLinear.MetricsWarmupPeriod+ ...
    IncrementalPCA.WarmupPeriod+ ...
    IncrementalPCA.EstimationPeriod)/numObsPerChunk,"g--")
legend(h,ce.Properties.VariableNames)
xlabel(t,"Iteration")

$Figure contains 3 axes objects. Axes object 1 with ylabel Top EV [%] contains 2 objects of type line, constantline. Axes object 2 with ylabel \beta_1 contains 2 objects of type line, constantline. Axes object 3 with ylabel Classification Error contains 3 objects of type line, constantline. These objects represent Cumulative, Window.$

The highest explained variance value is 0 during the estimation period and then rapidly rises to 73%. The value then gradually approaches 77%.

The plots suggest that updateMetricsAndFit performs these steps:

Fit $β_{1}$ after the estimation and warm-up periods only.
Compute the performance metrics after the estimation, warm-up, and metrics warm-up periods only.
Compute the cumulative metrics during each iteration.
Compute the window metrics after processing 200 observations (four iterations).

Predict Activity Labels Using Final Models

Transform the test data using the final incremental PCA model. Predict activity labels for the transformed test data using the final incremental linear classification model.

transformedXtest = transform(IncrementalPCA,Xtest);
predictedLabels = predict(IncrementalLinear,transformedXtest);

Create a confusion matrix for the test data.

figure
ConfusionTrain = confusionchart(Ytest,predictedLabels);

Figure contains an object of type ConfusionMatrixChart.

The final model misclassifies only 27 of 4075 observations in the test data.

Tips

You can create an incrementalPCA model object that incorporates the outputs of the pca function by using the following code:
```
[coeff,~,latent,~,~,mu]=pca(X);
incrementalMdl = incrementalPCA(Coefficients=coeff, ...
    Latent=latent,Means=mu,NumObservations=1000);
```
You can create an incrementalPCA model object that incorporates the outputs of the pcacov function by using the following code:
```
[coeff,latent]=pcacov(V);
incrementalMdl = incrementalPCA(Coefficients=coeff,Latent=latent);
```

Algorithms

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Estimation Period

During the estimation period, the incremental fit function does not fit the model. The function uses the first incoming EstimationPeriod observations to estimate the variable means (Mu) and standard deviations (Sigma). At the end of the estimation period, the function updates the properties that store the hyperparameters.

Estimation occurs only when:

EstimationPeriod is positive.
IncrementalMdl.Mu and IncrementalMdl.Sigma are empty arrays [].
The incremental PCA model object is configured to center or standardize the predictor data (see Center Data and Standardize Data).

Note

If you specify a positive EstimationPeriod and StandardizeData is false, then fit resets EstimationPeriod to 0.

Center Data

When you specify CenterData=true and a positive estimation period (see Estimation Period), and IncrementalMdl.Mu and IncrementalMdl.Sigma are empty, the incremental fit function estimates means using the estimation period observations.

Standardize Data

When you specify StandardizeData=true and a positive estimation period (see Estimation Period), and IncrementalMdl.Mu and IncrementalMdl.Sigma are empty, the incremental fit function estimates means and standard deviations using the estimation period observations.

When the incremental fit function estimates predictor means and standard deviations, it computes weighted means and weighted standard deviations using the estimation period observations. Specifically, the function standardizes predictor j (x_j) using

$x_{j}^{*} = \frac{x_{j} - μ_{j}^{*}}{σ_{j}^{*}} .$

x_j is predictor j, and x_jk is observation k of predictor j in the estimation period.
$μ_{j}^{*} = \frac{1}{\sum_{k} w_{k}} \sum_{k} w_{k} x_{j k} .$
${(σ_{j}^{*})}^{2} = \frac{1}{\sum_{k} w_{k}} \sum_{k} w_{k} {(x_{j k} - μ_{j}^{*})}^{2} .$
w_j is observation weight j.

References

[1] Ross, David A., Jongwoo Lim, Ruei-Sung Lin, and Ming-Hsuan Yang. "Incremental Learning for Robust Visual Tracking." International Journal of Computer Vision 77 (2008): 125-141.

Version History

Introduced in R2024a

incrementalPCA

Description

Creation

Syntax

Description

Input Arguments

`CenterData` — Flag to center data
`false` or `0` (default) | `true` or `1`

`Means` — Column means
numeric row vector

`NumObservations` — Number of observations
positive integer

`StandardizeData` — Flag to standardize predictor data
`false` or `0` (default) | `true` or `1`

Properties

`Coefficients` — Principal component coefficients
numeric matrix

`Latent` — Principal component variances
numeric column vector

`EstimationPeriod` — Number of observations processed to estimate hyperparameters
nonnegative integer

`ExplainedVariance` — Percentage of total variance explained
numeric column vector

`IsWarm` — Flag indicating whether `fit` returns transformed data
`false` or `0` | `true` or `1`

`Mu` — Predictor means
numeric vector | `[]`

`NumComponents` — Number of principal components
nonnegative integer

`NumPredictors` — Number of predictor variables
nonnegative integer

`NumTrainingObservations` — Number of observations fit to incremental model
nonnegative numeric scalar

`Sigma` — Predictor standard deviations
numeric vector | `[]`

`VariableWeights` — Variable weights
row vector of positive scalar values

`WarmupPeriod` — Number of observations to which model must be fit before it is warm
nonnegative integer

Object Functions

Examples

Perform Incremental Principal Component Analysis Using Initial Model

Perform Incremental Learning Incorporating Principal Component Analysis

Tips

Algorithms

Estimation Period

Center Data

Standardize Data

References

Version History

See Also

Functions

Topics

incrementalPCA

Description

Creation

Syntax

Description

Input Arguments

CenterData — Flag to center data false or 0 (default) | true or 1

Means — Column means numeric row vector

NumObservations — Number of observations positive integer

StandardizeData — Flag to standardize predictor data false or 0 (default) | true or 1

Properties

Coefficients — Principal component coefficients numeric matrix

Latent — Principal component variances numeric column vector

EstimationPeriod — Number of observations processed to estimate hyperparameters nonnegative integer

ExplainedVariance — Percentage of total variance explained numeric column vector

IsWarm — Flag indicating whether fit returns transformed data false or 0 | true or 1

Mu — Predictor means numeric vector | []

NumComponents — Number of principal components nonnegative integer

NumPredictors — Number of predictor variables nonnegative integer

NumTrainingObservations — Number of observations fit to incremental model nonnegative numeric scalar

Sigma — Predictor standard deviations numeric vector | []

VariableWeights — Variable weights row vector of positive scalar values

WarmupPeriod — Number of observations to which model must be fit before it is warm nonnegative integer

Object Functions

Examples

Perform Incremental Principal Component Analysis Using Initial Model

Perform Incremental Learning Incorporating Principal Component Analysis

Tips

Algorithms

Estimation Period

Center Data

Standardize Data

References

Version History

See Also

Functions

Topics

`CenterData` — Flag to center data
`false` or `0` (default) | `true` or `1`

`Means` — Column means
numeric row vector

`NumObservations` — Number of observations
positive integer

`StandardizeData` — Flag to standardize predictor data
`false` or `0` (default) | `true` or `1`

`Coefficients` — Principal component coefficients
numeric matrix

`Latent` — Principal component variances
numeric column vector

`EstimationPeriod` — Number of observations processed to estimate hyperparameters
nonnegative integer

`ExplainedVariance` — Percentage of total variance explained
numeric column vector

`IsWarm` — Flag indicating whether `fit` returns transformed data
`false` or `0` | `true` or `1`

`Mu` — Predictor means
numeric vector | `[]`

`NumComponents` — Number of principal components
nonnegative integer

`NumPredictors` — Number of predictor variables
nonnegative integer

`NumTrainingObservations` — Number of observations fit to incremental model
nonnegative numeric scalar

`Sigma` — Predictor standard deviations
numeric vector | `[]`

`VariableWeights` — Variable weights
row vector of positive scalar values

`WarmupPeriod` — Number of observations to which model must be fit before it is warm
nonnegative integer