Stochastic Optimization for Multiview Representation Learning using Partial Least Squares To cite this code: @inproceedings{arora2016stochastic, title={Stochastic optimization for multiview representation learning using partial least squares}, author={Arora, Raman and Mianjy, Poorya and Marinov, Teodor}, booktitle={Proceedings of The 33rd International Conference on Machine Learning}, pages={1786--1794}, year={2016} }
To run the code, please make the following folders: "CODE", "DATA", "PAGE", "PLOTS", "REPORT", and download the code to the "CODE" folder.
The actual data should be in "DATA" folder, in the form of a struct with the following filds: data.view1.training, data.view1.tuning, data.view1.testing, data.view2.training, data.view2.tuning, data.view2.testing each of which is a "di x Nj" matrix, i={1,2}, j={1,2,3}, where Nj is the number of samples and dj is the dimension. In addition to the data, a "perm" matrix should be included in the "DATA" folder which basically stores random permutations over the samples. For example, this is a code to generate such a permutation matrix: perm=zeros(1000,N); for i=1:1000 perm(i,:)=randperm(N); end save permdata.mat perm
Please see the "Demo.m" for a simple demonstration.