no code implementations • 4 Nov 2013 • Martin Takáč, Selin Damla Ahipaşaoğlu, Ngai-Man Cheung, Peter Richtárik
Our approach attacks the maximization problem in sparse PCA directly and is scalable to high-dimensional data.
1 code implementation • 17 Dec 2012 • Peter Richtárik, Majid Jahani, Selin Damla Ahipaşaoğlu, Martin Takáč
Given a multivariate data set, sparse principal component analysis (SPCA) aims to extract several linear combinations of the variables that together explain the variance in the data as much as possible, while controlling the number of nonzero loadings in these combinations.
