Kernel Alignment for Unsupervised Feature Selection via Matrix Factorization

arXiv:2403.14688v1 Announce Type: new
Abstract: By removing irrelevant and redundant features, feature selection aims to find a good representation of the original features. With the prevalence of unlabeled data, unsupervised feature selection has been proven effective in alleviating the so-called curse of dimensionality. Most existing matrix factorization-based unsupervised feature selection methods are built upon subspace learning, but they have limitations in capturing nonlinear structural information among features. It is well-known that kernel techniques can capture nonlinear structural information. In this …

abstract alignment arxiv cs.lg cs.na data dimensionality factorization feature features feature selection good kernel math.na matrix representation type unsupervised via