Autoencoder. Autoencoder is a classic type of neural networks for unsupervised learning. a. Demonstrate that PCA is

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Autoencoder. Autoencoder is a classic type of neural networks for unsupervised learning.

a. Demonstrate that PCA is a special case of the autoencoder by showing that the loss function of PCA is equivalent to the mean-squared error of the autoencoder with linear activation, where the encoder and decoder share the same parameters.

b. Implement an autoencoder with nonlinear activation, and use (1) mean-squared-error and (2) binary cross-entropy as the loss function. Note that when using the binary cross-entropy

loss, each data point should be normalized into [0,1]. Compare them with PCA on the MNIST data set, which can be found at http://yann.lecun.com/exdb/mnist/, in terms of classification accuracy and the visualization of reconstructed images. For the classification task, first use the autoencoder or PCA to obtain the features of the data and then train a linear SVM or logistic regression model using the features.

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Data Mining Concepts And Techniques

ISBN: 9780128117613

4th Edition

Authors: Jiawei Han, Jian Pei, Hanghang Tong

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