Clustering spherical shells by a mini-max information algorithm

Authors:
Xulei Yang;Qing Song;Wenbo Zhang;Zhimin Wang
Affiliations:
School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore;School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore;School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore;School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore
Venue:
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Year:
2006

Citing 4
Cited 0

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Abstract

We focus on spherical shells clustering by a mini-max information (MMI) clustering algorithm based on mini-max optimization of mutual information (MI). The minimization optimization leads to a mass constrained deterministic annealing (DA) approach, which is independent of the choice of the initial data configuration and has the ability to avoid poor local optima. The maximization optimization provides a robust estimation of probability soft margin to phase out outliers. Furthermore, a novel cluster validity criteria is estimated to determine an optimal cluster number of spherical shells for a given set of data. The effectiveness of MMI algorithm for clustering spherical shells is demonstrated by experimental results.