Robust data clustering in mercer kernel-induced feature space

Authors:
Xulei Yang;Qing Song;Meng-Joo Er
Affiliations:
EEE School, Nanyang Technological University, Singapore;EEE School, Nanyang Technological University, Singapore;EEE School, Nanyang Technological University, Singapore
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

Citing 3
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Abstract

In this paper, we focus on developing a new clustering method, robust kernel-based deterministic annealing (RKDA) algorithm, for data clustering in mercer kernel-induced feature space. A nonlinear version of the standard deterministic annealing (DA) algorithm is first constructed by means of a Gaussian kernel, which can reveal the structure in the data that may go unnoticed if DA is performed in the original input space. After that, a robust pruning method, the maximization of the mutual information against the constrained input data points, is performed to phase out noise and outliers. The good aspects of the proposed method for data clustering are supported by experimental results.