Name
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 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. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1016/S0893-6080(01)00020-X | Neural Networks |
Keywords | Field | DocType |
neural networks,competitive learning,online learning,image reconstruction,online vector quantization,neural trees,self-organizing tree,hierarchical clustering,image compression,cost function,neural network,unsupervised learning,data clustering,tree structure | Hierarchical clustering,Competitive learning,Pattern recognition,Linde–Buzo–Gray algorithm,Unsupervised learning,Vector quantization,Artificial intelligence,Quantization (signal processing),Cluster analysis,Machine learning,Mathematics,Image compression | Journal |
Volume | Issue | ISSN |
14 | 4-5 | 0893-6080 |
Citations | PageRank | References |
28 | 1.38 | 34 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M M Campos | 1 | 104 | 6.56 |
| Gail A. Carpenter | 2 | 2909 | 760.83 |