Learning with ℓ1 -graph for high dimensional data analysis

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

doi:10.1007/978-1-4614-4457-2_6

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 proceeding › Chapter

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 language	English (US)
Title of host publication	Graph Embedding for Pattern Analysis
Publisher	Springer
Pages	139-156
Number of pages	18
ISBN (Electronic)	9781461444572
ISBN (Print)	9781461444565
DOIs	https://doi.org/10.1007/978-1-4614-4457-2_6
State	Published - Jan 1 2013
Externally published	Yes

ASJC Scopus subject areas

General Engineering

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10.1007/978-1-4614-4457-2_6

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Learning with ℓ1 -graph for high dimensional data analysis

Abstract

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