Structured learning for unsupervised feature selection with high-order matrix factorization
Abstract
Feature selection aims at searching the most discriminative and relevant features from high-dimensional data to improve the performance of certain learning tasks. Whereas, irrelevant or redundant features may increase the over-fitting risk of consequent learning algorithms. Structured learning of feature selection is to embed intrinsic structures of data, such as geometric structures and manifold structures, resulting in the improvement of...
Paper Details
Title
Structured learning for unsupervised feature selection with high-order matrix factorization
Published Date
Feb 1, 2020
Volume
140
Pages
112878 - 112878
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