Unsupervised Feature Selection via Local Total-Order Preservation

Pages: 16 - 28
Published: Jan 1, 2019
Abstract
Without class label, unsupervised feature selection methods choose a subset of features that faithfully maintain the intrinsic structure of original data. Conventional methods assume that the exact value of pairwise samples distance used in structure regularization is effective. However, this assumption imposes strict restrictions to feature selection, and it causes more features to be kept for data representation. Motivated by this, we propose...
Paper Details
Title
Unsupervised Feature Selection via Local Total-Order Preservation
Published Date
Jan 1, 2019
Pages
16 - 28
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