2016
Optimal Sparse Linear Encoders and Sparse PCA
NeurIPS 2016poster
Principal components analysis~(PCA) is the optimal linear encoder of data. Sparse linear encoders (e.g., sparse PCA) produce more interpretable features that can promote better generalization. (\rn{1}) Given a level of sparsity, what is the best approximation to PCA? (\rn{2}) Are there efficient…