Extending the SOM algorithm to non-Euclidean distances via the kernel trick

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3316, Issue , 2004, Pages 150-157

  Martín Merino, Manuel ^a   Muñoz, Alberto ^b  

^a UNIVERSIDAD PONTIFICIA DE SALAMANCA   (Spain)

^b UNIVERSIDAD CARLOS III DE MADRID   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING ALGORITHMS; CONFORMAL MAPPING; SELF ORGANIZING MAPS; SPEECH RECOGNITION;

EUCLIDEAN DISTANCE; FEATURE SPACE; HIGH DIMENSIONAL DATA; MERCER KERNEL; NON-EUCLIDEAN DISTANCE; NONLINEAR PROJECTIONS; ORIGINAL ALGORITHMS; SOM ALGORITHMS;

ALGORITHMS;

EID: 33750540093     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-30499-9_22     Document Type: Article

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