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Unsupervised Feature Selection via Multi-step Markov Transition Probability
[article]
2020
arXiv
pre-print
Feature selection is a widely used dimension reduction technique to select feature subsets because of its interpretability. Many methods have been proposed and achieved good results, in which the relationships between adjacent data points are mainly concerned. But the possible associations between data pairs that are may not adjacent are always neglected. Different from previous methods, we propose a novel and very simple approach for unsupervised feature selection, named MMFS (Multi-step
arXiv:2005.14359v1
fatcat:5rs2v2d2rzhqnjvk4auluzwncu