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Sparse eigen methods by D.C. programming
2007
Proceedings of the 24th international conference on Machine learning - ICML '07
Eigenvalue problems are rampant in machine learning and statistics and appear in the context of classification, dimensionality reduction, etc. In this paper, we consider a cardinality constrained variational formulation of generalized eigenvalue problem with sparse principal component analysis (PCA) as a special case. Using ℓ 1 -norm approximation to the cardinality constraint, previous methods have proposed both convex and non-convex solutions to the sparse PCA problem. In contrast, we propose
doi:10.1145/1273496.1273601
dblp:conf/icml/SriperumbudurTL07
fatcat:ggjnftjdcbgmdpqgiygc6ykdni