Learning with ℓ1 -graph for high dimensional data analysis

Jianchao Yang, Bin Cheng, Shuicheng Yan, Yun Fu, Thomas Huang

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

An informative graph, directed or undirected, is critical for those graph-orientated algorithms designed for data analysis, such as clustering, subspace learning, and semi-supervised learning. Data clustering often starts with a pairwise similarity graph and then translates into a graph partition problem [19], and thus the quality of the graph essentially determines the clustering quality.

Original languageEnglish (US)
Title of host publicationGraph Embedding for Pattern Analysis
PublisherSpringer
Pages139-156
Number of pages18
ISBN (Electronic)9781461444572
ISBN (Print)9781461444565
DOIs
StatePublished - Jan 1 2013

ASJC Scopus subject areas

  • General Engineering

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