Sparse PCA via Bipartite Matchings

NeurIPS 2015 Megasthenis AsterisDimitris PapailiopoulosAnastasios KyrillidisAlexandros G. Dimakis

We consider the following multi-component sparse PCA problem: given a set of data points, we seek to extract a small number of sparse components with disjoint supports that jointly capture the maximum possible variance. These components can be computed one by one, repeatedly solving the single-component problem and deflating the input data matrix, but as we show this greedy procedure is suboptimal... (read more)

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