Publications - DO NOT EDIThttp://hdl.handle.net/10106/50652024-03-29T11:28:45Z2024-03-29T11:28:45ZBranch-and-Bound for Model Selection and its Computational ComplexityThakoor, Ninad ShashikantGao, Jeanhttp://hdl.handle.net/10106/50722023-10-19T18:04:00Z2010-09-02T00:00:00ZBranch-and-Bound for Model Selection and its Computational Complexity
Thakoor, Ninad Shashikant; Gao, Jean
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.
2010-09-02T00:00:00Z