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  • 2020-07-29 07:54
Deep Canonical correlation analysis (DCCA)
MATLAB package for Deep Canonically Correlated Autoencoders (DCCAE) (C) 2015 by Weiran Wang, Raman Arora, Karen Livescu and Jeff Bilmes This Matlab code implements the Deep Canonically Correlated Autoencoders (DCCAE) algorithm described in the paper: Weiran Wang, Raman Arora, Karen Livescu, and Jeff Bilmes. On Deep Multi-View Representation Learning. The 32nd International Conference on Machine Learning (ICML 2015). Quick start: - demo.m: demonstrates the usage of linear/kernel/deep CCA/DCCAE on noisy MNIST images (random rotation and background) as two views. List of functions: - DCCA_grad.m: computes the gradient of total correlation with respect to the neural network weights for each view. - DCCA_train.m: DCCA training with stochastic minibatch optimization. - DCCAE_train.m: DCCAE training with stochastic minibatch optimization. - linCCA.m: linear CCA. - randKCCA.m: randomized kernel CCA, proposed in David Lopez-Paz, Suvrit Sra, Alex Smola, Zoubin Ghahramani, and Bernhard Schoelkopf. Randomized Nonlinear Component Analysis. The 31th International Conference on Machine Learning (ICML) 2014. - svmclassify.m: one-versus-one linear SVM by calling libsvm. The libsvm package is available from http://www.csie.ntu.edu.tw/~cjlin/libsvm/. - createMNIST.m: generates the MNIST images used in demo.m (the random seed used to generate our data is saved in demoseed.mat, so that you can achieve exactly the same result). The following are used internally by other functions (in ./deepnet) - deepnetfwd.m: feed-forwards the inputs through deep neural networks to obtain outputs. - deepnetinit.m: initializes the neural network weight parameters. - deepnetgrad.m: computes gradient of reconstruction error with respect to neural network weights. External packages/data: - mnist_all.mat: the MATLAB format of all MNIST images, can be downloaded from Sam Roweis's webpage http://www.cs.nyu.edu/~roweis/data.html. - RBMPRETRAIN_K=10.mat: pretrained deep autocencoders using restricted Boltzmann machines (RBMs), we use the following code for pretraining: http://www.cs.toronto.edu/~hinton/MatlabForSciencePaper.html Reference: G. E. Hinton and R. R. Salakhutdinov. Reducing the Dimensionality of Data with Neural Networks. Science, 2006.