Stochastic K-means algorithm for vector quantization

Pattern Recognition Letters

Volumn 22, Issue 6-7, 2001, Pages 603-610

  Kövesi, Balazs ^a   Boucher, Jean Marc ^b   Saoudi, Samir ^b  

^a ORANGE LABS   (France)

^b ECOLE DES MINES DE NANTES   (France)

Author keywords

K means; Stochastic methods; Vector quantization

Indexed keywords

ALGORITHMS; PROBABILITY DISTRIBUTIONS; RANDOM PROCESSES; VECTOR QUANTIZATION;

CLUSTER GRAVITY CENTER;

OBJECT RECOGNITION;

PATTERN RECOGNITION;

EID: 0035336869     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-8655(01)00021-6     Document Type: Article

References (8)

1. Cluster analysis of multivariate data: Efficiency vs interpretability of classifications
2. Line spectrum frequency based distance measures for speech recognition
3. Fast LSP vector quantization algorithms comparison
4. A new efficient algorithm to compute the LSP parameters for speech coding
5. Competitive learning and soft competition for vector quantizer design
6. Globally optimal vector quantizer design by stochastic relaxation