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Campos M.M., Carpenter G.A. — S-TREE: self-organizing trees for data clustering and online
Campos M.M., Carpenter G.A. — S-TREE: self-organizing trees for data clustering and online

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Название: S-TREE: self-organizing trees for data clustering and online

Авторы: Campos M.M., Carpenter G.A.

Аннотация:

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 GLA while 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. © 2001 Elsevier Science Ltd. All rights reserved.


Язык: en

Рубрика: Computer science/

Тип: Статья

Статус предметного указателя: Неизвестно

ed2k: ed2k stats

Год издания: 2000

Количество страниц: 21

Добавлена в каталог: 11.09.2006

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