Fast Sparse PCA via Positive Semidefinite Projection for Unsupervised Feature Selection
Junjing Zheng,
Xinyu Zhang,
Weidong Jiang
et al.
Abstract:<p>In the field of unsupervised feature selection, sparse principal component analysis (SPCA) methods have attracted more and more attention recently. Compared to spectral-based methods, SPCA methods don’t rely on the construction of a similarity matrix and show better feature selection ability on real-world data. Existing convex SPCA methods reformulate SPCA as a convex model by regarding the reconstruction matrix as an optimization variable. However, they are lack of constraints equivalent to the ortho… Show more
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