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S-TREE: Self-Organizing Trees for Data Clustering and Online Vector Quantization

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dc.contributor.author Campos, Marcos en_US
dc.contributor.author Carpenter, Gail en_US
dc.date.accessioned 2011-11-14T19:02:49Z
dc.date.available 2011-11-14T19:02:49Z
dc.date.issued 2000-09 en_US
dc.identifier.uri http://hdl.handle.net/2144/2275
dc.description.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. S-TREE2 implements an online procedure that approximates an optimal (unstructured) clustering solution while imposing a tree-structure constraint. 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. en_US
dc.description.sponsorship Office of Naval Research (N00014-95-10409, N00014-95-0G57) en_US
dc.language.iso en_US en_US
dc.publisher Boston University Center for Adaptive Systems and Department of Cognitive and Neural Systems en_US
dc.relation.ispartofseries BU CAS/CNS Technical Reports;CAS/CNS-TR-2000-028 en_US
dc.rights Copyright 2000 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission. en_US
dc.subject Hierarchical clustering en_US
dc.subject Online vector quantization en_US
dc.subject Competitive learning en_US
dc.subject Online learning en_US
dc.subject Neural trees en_US
dc.subject Neural networks en_US
dc.subject Image reconstruction en_US
dc.subject Image compression en_US
dc.title S-TREE: Self-Organizing Trees for Data Clustering and Online Vector Quantization en_US
dc.type Technical Report en_US
dc.rights.holder Boston University Trustees en_US


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