A topological view of unsupervised learning from noisy data

SIAM Journal on Computing

Volumn 40, Issue 3, 2011, Pages 646-663

  Niyogi, P ^a   Smale, S ^b   Weinberger, S ^c  

^a THE UNIVERSITY OF CHICAGO   (United States)

^b UNIVERSITY OF HONG KONG   (Hong Kong)

^c UNIVERSITY OF CHICAGO   (United States)

Author keywords

Data; Manifolds; Topology

Indexed keywords

CONNECTED COMPONENT; DATA; FINITE SET; HIGH CONFIDENCE; HIGH DIMENSIONS; LAPLACIANS; LOW-DIMENSIONAL MANIFOLDS; MIXTURE OF GAUSSIANS; MODELING DATA; NOISY DATA; SIMPLICIAL COMPLEX; SPECTRAL LEARNING ALGORITHMS;

GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); LEARNING ALGORITHMS; UNSUPERVISED LEARNING;

TOPOLOGY;

EID: 79960379431     PISSN: 00975397     EISSN: None     Source Type: Journal    
DOI: 10.1137/090762932     Document Type: Article

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