To train an autoencoder for dimensionality reduction on your high-dimensional time-series dataset, you can follow these steps in MATLAB. The dlarray format is useful for handling multi-dimensional arrays, and MATLAB's Deep Learning Toolbox provides tools to work with such data.
Here’s a step-by-step guide:
Step 1: Prepare the Data
Make sure your data is in the correct format. Assuming you have a 4D dlarray where the dimensions are arranged as [features, time, batch, channels].
Step 2: Define the Autoencoder Architecture
Define the architecture of your autoencoder. The encoder part will compress the input data to a lower-dimensional representation, and the decoder part will reconstruct the input data from this lower-dimensional representation.
Step 3: Train the Autoencoder
Use the trainNetwork function to train your autoencoder with the specified architecture and training options.
Here’s an example code snippet to illustrate these steps:
% Assuming your data is in a 4D dlarray format
% data: [features, time, batch, channels]
% Example data dimensions
numFeatures = 625;
numTimeSteps = 50000;
numBatches = 10; % Example number of batches
numChannels = 1; % Example number of channels
% Load your data (replace this with your actual data loading code)
data = randn(numFeatures, numTimeSteps, numBatches, numChannels, 'single');
data = dlarray(data, 'CBTC'); % 'CBTC' stands for 'Channel', 'Batch', 'Time', 'Channel'
% Define the autoencoder architecture
inputSize = [numFeatures, numTimeSteps, numChannels];
% Encoder
encoderLayers = [
imageInputLayer(inputSize, 'Name', 'input', 'Normalization', 'none')
convolution2dLayer([3, 3], 16, 'Padding', 'same', 'Name', 'conv1')
reluLayer('Name', 'relu1')
maxPooling2dLayer([2, 2], 'Stride', [2, 2], 'Name', 'maxpool1')
convolution2dLayer([3, 3], 8, 'Padding', 'same', 'Name', 'conv2')
reluLayer('Name', 'relu2')
fullyConnectedLayer(50, 'Name', 'fc1') % Reduce to 50 features (example)
];
% Decoder
decoderLayers = [
fullyConnectedLayer(prod([numFeatures, numTimeSteps, numChannels]), 'Name', 'fc2')
reluLayer('Name', 'relu3')
transposedConv2dLayer([3, 3], 8, 'Cropping', 'same', 'Name', 'deconv1')
reluLayer('Name', 'relu4')
transposedConv2dLayer([3, 3], 16, 'Cropping', 'same', 'Name', 'deconv2')
reluLayer('Name', 'relu5')
transposedConv2dLayer([3, 3], numChannels, 'Cropping', 'same', 'Name', 'deconv3')
regressionLayer('Name', 'output')
];
% Combine encoder and decoder
layers = [
encoderLayers
decoderLayers
];
% Specify training options
options = trainingOptions('adam', ...
'MaxEpochs', 50, ...
'InitialLearnRate', 1e-3, ...
'MiniBatchSize', 128, ...
'Shuffle', 'every-epoch', ...
'Plots', 'training-progress', ...
'Verbose', false);
% Train the autoencoder
net = trainNetwork(data, data, layers, options);
% Extract the encoder part of the network
encoderNet = layerGraph(net.Layers(1:numel(encoderLayers)));
% Save the trained network
save('autoencoderNet.mat', 'net', 'encoderNet');
Notes:
- Adjust the architecture according to your specific needs. The example provided is a simple convolutional autoencoder.
- Modify the number of layers, filter sizes, and the number of features in the fully connected layer to fit your dataset and desired dimensionality reduction.
- Ensure your data is normalized appropriately before feeding it into the network.
This approach should help you train an autoencoder to reduce the dimensionality of your high-dimensional time-series dataset.
