Clustering with similarity preserving
Abstract
Graph-based clustering has shown promising performance in many tasks. A key step of graph-based approach is the similarity graph construction. In general, learning graph in kernel space can enhance clustering accuracy due to the incorporation of nonlinearity. However, most existing kernel-based graph learning mechanisms is not similarity-preserving, hence leads to sub-optimal performance. To overcome this drawback, we propose a more...
Paper Details
Title
Clustering with similarity preserving
Published Date
Nov 1, 2019
Journal
Volume
365
Pages
211 - 218
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