Branch-and-Bound for Model Selection and its Computational Complexity

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Branch-and-Bound for Model Selection and its Computational Complexity

Show simple item record Thakoor, Ninad Gao, Jean 2010-10-12T18:56:34Z 2010-10-12T18:56:34Z 2010-09-02
dc.identifier.citation Published in IEEE Transactions on Knowledge and Data Engineering, issue 99 en_US
dc.identifier.issn 1041-4347
dc.description.abstract Branch-and-bound methods are used in various data analysis problems such as clustering, seriation and feature selection. Classical approaches of branch-and-bound based clustering search through combinations of various partitioning possibilities to optimize a clustering cost. However, these approaches are not practically useful for clustering of image data where the size of data is large. Additionally, the number of clusters is unknown in most of the image data analysis problems. By taking advantage of the spatial coherency of clusters, we formulate an innovative branch-and-bound approach which solves clustering problem as a model selection problem. In this generalized approach, cluster parameter candidates are first generated by spatially coherent sampling. A branch-and-bound search is carried out through the candidates to select an optimal subset. This paper formulates this approach and investigates its average computational complexity. Improved clustering quality and robustness to outliers compared to conventional iterative approach are demonstrated with experiments. en_US
dc.description.sponsorship IEEE Computer Society en_US
dc.language.iso en_US en_US
dc.publisher IEEE Xplore en_US
dc.subject Clustering en_US
dc.subject Segmentation en_US
dc.subject Combinatorial optimization en_US
dc.subject Branch-and-bound en_US
dc.subject Model selection en_US
dc.title Branch-and-Bound for Model Selection and its Computational Complexity en_US
dc.type Article en_US
dc.publisher.department University of Texas at Arlington, Department of Computer Science Engineering
dc.identifier.externalLink en_US
dc.identifier.externalLinkDescription Link to Research Profiles en_US

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