How to train dataset after dimension reduced with autoencoder into a support vector machine?

Question

Ng Yong Jie 2022-2-5

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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1643625-how-to-train-dataset-after-dimension-reduced-with-autoencoder-into-a-support-vector-machine

评论： Ng Yong Jie 2022-2-9

I am doing a anomaly detection by using stacked autoencoder and one-class svm. The stacked autoencoder is done. The problem is I am unsure of how to use the dimension reduced data after the autoencoder to be trained in the one-class svm?

Here's the code for the stacked autoencoder:

train1 = fullfile(dataFolder, "train_FD001.txt");

[train_data1, train_labels1] = importdata(train1)

% To avoid results from training are different each time

% Explicitly set the random number generator seed

rng('default')

% Specifying the values for the regularizers for the training

%autoenc1 = trainAutoencoder(train_data1,hiddenSize1, ...

autoenc1 = trainAutoencoder(train_data1,100,'MaxEpochs',400,'L2WeightRegularization',1.0, ...

'DecoderTransferFunction','purelin','UseGPU',true)

% Visualize the first autoencoder

view(autoenc1)

% Train the next autoencoder on a set of these vectors extracted from the training data.

% First, you must use the encoder from the trained autoencoder to generate the features.

feat1 = encode(autoenc1,train_data1)

% Specifying the values for the regularizers for the training

autoenc2 = trainAutoencoder(feat1,10,'MaxEpochs',400,...

'DecoderTransferFunction','purelin','UseGPU',true)

% Visualize the second autoencoder

view(autoenc2)

% Train the next autoencoder on a set of these vectors extracted from the training data.

% First, you must use the encoder from the trained autoencoder to generate the features.

feat2 = encode(autoenc2,feat1)

% Specifying the values for the regularizers for the training

autoenc3 = trainAutoencoder(feat2,1,'MaxEpochs',400,...

'DecoderTransferFunction','purelin','UseGPU',true)

% Visualize the third autoencoder

view(autoenc3)

% Train the next autoencoder on a set of these vectors extracted from the training data.

% First, you must use the encoder from the trained autoencoder to generate the features.

feat3 = encode(autoenc3,feat2)

%% Training the final softmax layer

% Train a softmax layer to classify the 50-dimensional feature vectors.

% Unlike the autoencoders, you train the softmax layer in a supervised fashion using labels for the training data.

softnet = trainSoftmaxLayer(feat3,train_data1,'MaxEpochs',400)

% Vizualize the diagram of the softmax layer

view(softnet)

%% Forming a stacked neural network

stackednet = stack(autoenc1,autoenc2,autoenc3,softnet)

% Vizualize the stacked autoencoder

view(stackednet)

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Answer 1

yanqi liu 2022-2-8

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1643625-how-to-train-dataset-after-dimension-reduced-with-autoencoder-into-a-support-vector-machine#answer_890955

yes，sir，may be use the encoder net to get data low dimension vector as feature，reshape them to feature matrix and label vector，then train it in new svm model