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Titolo:
S-TREE: self-organizing trees for data clustering and online vector quantization
Autore:
Campos, MM; Carpenter, GA;
Indirizzi:
Boston Univ, Dept Cognit & Neural Syst, Ctr Adapt Syst, Boston, MA 02215 USA Boston Univ Boston MA USA 02215 yst, Ctr Adapt Syst, Boston, MA 02215 USA
Titolo Testata:
NEURAL NETWORKS
fascicolo: 4-5, volume: 14, anno: 2001,
pagine: 505 - 525
SICI:
0893-6080(200105)14:4-5<505:SSTFDC>2.0.ZU;2-#
Fonte:
ISI
Lingua:
ENG
Soggetto:
ALGORITHM; QUANTIZERS; NETWORK; DESIGN;
Keywords:
hierarchical clustering; online vector quantization; competitive learning; online learning; neural trees; neural networks; image reconstruction; image compression;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
Citazioni:
50
Recensione:
Indirizzi per estratti:
Indirizzo: Carpenter, GA Boston Univ, Dept Cognit & Neural Syst, Ctr Adapt Syst, 677 Beacon St, Boston, MA 02215 USA Boston Univ 677 Beacon St Boston MA USA 02215 , MA 02215 USA
Citazione:
M.M. Campos e G.A. Carpenter, "S-TREE: self-organizing trees for data clustering and online vector quantization", NEURAL NETW, 14(4-5), 2001, pp. 505-525

Abstract

This paper introduces S-TREE (Self-Organizing Tree), a family of models that use unsupervised learning to construct hierarchical representations of data and online tree-structured vector quantizers. The S-TREE1 model, which features a new tree-building algorithm, can be implemented with various cost functions. An alternative implementation, S-TREE2, which uses a new double-path search procedure, is also developed. The performance of the S-TREE algorithms is illustrated with data clustering and vector quantization examples, including a Gauss-Markov source benchmark and an image compression application. S-TREE performance on these tasks is compared with the standard tree-structured vector quantizer (TSVQ) and the generalized Lloyd algorithm (GLA). The image reconstruction quality with S-TREE2 approaches that of GLAwhile taking less than 10% of computer time. S-TREE1 and S-TREE2 also compare favorably with the standard TSVQ in both the time needed to create the codebook and the quality of image reconstruction. (C) 2001 Elsevier ScienceLtd. All rights reserved.

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Documento generato il 23/01/20 alle ore 06:31:03