%0 Conference Proceedings
%B Neural Information Processing Systems (NIPS'08)
%D 2008
%T Deflation Methods for Sparse PCA
%A Lester Mackey
%C Vancouver, Canada
%P 1-8
%U http://books.nips.cc/papers/files/nips21/NIPS2008_0197.pdf
%X In analogy to the PCA setting, the sparse PCA problem is often solved by iter- atively alternating between two subtasks: cardinality-constrained rank-one vari- ance maximization and matrix deﬂation. While the former has received a great deal of attention in the literature, the latter is seldom analyzed and is typically borrowed without justiﬁcation from the PCA context. In this work, we demon- strate that the standard PCA deﬂation procedure is seldom appropriate for the sparse PCA setting. To rectify the situation, we ﬁrst develop several deﬂation al- ternatives better suited to the cardinality-constrained context. We then reformulate the sparse PCA optimization problem to explicitly reﬂect the maximum additional variance objective on each round. The result is a generalized deﬂation procedure that typically outperforms more standard techniques on real-world datasets.
%8 12/2008