On the convergence of spectral clustering on random samples: The normalized case

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3120, Issue , 2004, Pages 457-471

  Von Luxburg, Ulrike ^a   Bousquet, Olivier ^a   Belkin, Mikhail ^b  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

^b THE UNIVERSITY OF CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA ACQUISITION; EIGENVALUES AND EIGENFUNCTIONS; GRAPH THEORY; LAPLACE TRANSFORMS; MATRIX ALGEBRA; PROBABILITY DISTRIBUTIONS; PROBLEM SOLVING;

DATA POINTS; LAPLACE OPERATORS; RANDOM SAMPLES; SPECTRAL CLUSTERING; DATA SPACE; GRAPH LAPLACIAN; NORMALIZED GRAPH LAPLACIAN; RANDOM SAMPLE; SAMPLE POINT; SAMPLE SIZES; STRONG CONVERGENCE;

LEARNING SYSTEMS; CLUSTERING ALGORITHMS;

EID: 9444238022     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-27819-1_32     Document Type: Conference Paper

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