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降维和特征提取

PCA、因子分析、特征选择、特征提取等

特征转换方法可以通过将数据转换为新特征来减少数据的维度。当无法转换变量时(例如,当数据中存在分类变量时),最好使用特征选择方法。有关特别适用于最小二乘拟合的特征选择方法,请参阅逐步回归

实时编辑器任务

降低维度Reduce dimensionality using Principal Component Analysis (PCA) in Live Editor

函数

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fscchi2Univariate feature ranking for classification using chi-square tests
fscmrmrRank features for classification using minimum redundancy maximum relevance (MRMR) algorithm
fscncaFeature selection using neighborhood component analysis for classification
fsrftestUnivariate feature ranking for regression using F-tests
fsrmrmrRank features for regression using minimum redundancy maximum relevance (MRMR) algorithm
fsrncaFeature selection using neighborhood component analysis for regression
fsulaplacianRank features for unsupervised learning using Laplacian scores
partialDependenceCompute partial dependence
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
oobPermutedPredictorImportancePredictor importance estimates by permutation of out-of-bag predictor observations for random forest of classification trees
oobPermutedPredictorImportancePredictor importance estimates by permutation of out-of-bag predictor observations for random forest of regression trees
predictorImportanceEstimates of predictor importance for classification tree
predictorImportanceEstimates of predictor importance for classification ensemble of decision trees
predictorImportanceEstimates of predictor importance for regression tree
predictorImportanceEstimates of predictor importance for regression ensemble
relieffRank importance of predictors using ReliefF or RReliefF algorithm
sequentialfsSequential feature selection using custom criterion
stepwiselmPerform stepwise regression
stepwiseglmCreate generalized linear regression model by stepwise regression
ricaFeature extraction by using reconstruction ICA
sparsefiltFeature extraction by using sparse filtering
transformTransform predictors into extracted features
tsnet-Distributed Stochastic Neighbor Embedding
barttestBartlett’s test
canoncorrCanonical correlation
pca原始数据的主成分分析
pcacovPrincipal component analysis on covariance matrix
pcaresResiduals from principal component analysis
ppcaProbabilistic principal component analysis
factoranFactor analysis
rotatefactorsRotate factor loadings
nnmfNonnegative matrix factorization
cmdscaleClassical multidimensional scaling
mahalMahalanobis distance to reference samples
mdscaleNonclassical multidimensional scaling
pdist成对观测值之间的两两距离
squareformFormat distance matrix
procrustesProcrustes analysis

对象

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FeatureSelectionNCAClassificationFeature selection for classification using neighborhood component analysis (NCA)
FeatureSelectionNCARegressionFeature selection for regression using neighborhood component analysis (NCA)
ReconstructionICAFeature extraction by reconstruction ICA
SparseFilteringFeature extraction by sparse filtering

主题

特征选择

特征提取

t-SNE 多维可视化

  • t-SNE
    t-SNE is a method for visualizing high-dimensional data by nonlinear reduction to two or three dimensions, while preserving some features of the original data.
  • Visualize High-Dimensional Data Using t-SNE
    This example shows how t-SNE creates a useful low-dimensional embedding of high-dimensional data.
  • tsne Settings
    This example shows the effects of various tsne settings.
  • t-SNE Output Function
    Output function description and example for t-SNE.

PCA 和典型相关

因子分析

  • Factor Analysis
    Factor analysis is a way to fit a model to multivariate data to estimate interdependence of measured variables on a smaller number of unobserved (latent) factors.
  • Analyze Stock Prices Using Factor Analysis
    Use factor analysis to investigate whether companies within the same sector experience similar week-to-week changes in stock prices.
  • Perform Factor Analysis on Exam Grades
    This example shows how to perform factor analysis using Statistics and Machine Learning Toolbox™.

非负矩阵分解

多维尺度分析

普氏分析