The principal components analysis of a graph, and its relationships to spectral clustering

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

Volumn 3201, Issue , 2004, Pages 371-383

  Saerens, Marco ^a   Fouss, Francois ^a   Yen, Luh ^a   Dupont, Pierre ^a  

^a UNIVERSITÉ CATHOLIQUE DE LOUVAIN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; GEODESY; MATRIX ALGEBRA; PATTERN RECOGNITION; PRINCIPAL COMPONENT ANALYSIS; PROBABILITY; ARTIFICIAL INTELLIGENCE; CLUSTERING ALGORITHMS; LEARNING SYSTEMS; MARKOV PROCESSES; RANDOM PROCESSES;

EUCLIDEAN COMMUTE TIME DISTANCE (ECTD); EUCLIDEAN SPACE; LAPLACIAN MATRIX; SPECTRAL CLUSTERING; TRANSITION PROBABILITIES; EUCLIDEAN COMMUTE TIME DISTANCES; LAPLACIAN MATRICES; MARKOV CHAIN MODELS; PRINCIPAL COMPONENTS ANALYSIS; SPECTRAL GRAPH THEORY; SUBSPACE PROJECTION;

GRAPH THEORY;

EID: 22944459214     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-30115-8_35     Document Type: Conference Paper

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