S-TREE: self-organizing trees for data clustering and online vector quantization.
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.
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Center for Adaptive Systems, Department of Cognitive and Neural Systems, Boston University, MA 02215, USA. gail@cns.bu.edu
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D000465:Algorithms; D001185:Artificial Intelligence; D003663:Decision Trees; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012985:Software Design