Abstract
Recently, the Rival Penalized Expectation-Maximization (RPEM) algorithm (Cheung 2004 & 2005) has demonstrated its outstanding capability to perform the model selection automatically in the context of density mixture models. Nevertheless, the RPEM is unable to exclude the irrelevant variables (also called features) from the clustering process, which may degrade the algorithm’s performance. In this paper, we adopt the concept of feature salience (Law et al. 2004) as the feature weight to measure the relevance of features to the cluster structure in the subspace, and integrate it into the RPEM algorithm. The proposed algorithm identifies the irrelevant features and estimates the number of clusters automatically and simultaneously in a single learning paradigm. Experiments show the efficacy of the proposed algorithm on both synthetic and benchmark real data sets.
This work was fully supported by the Research Grant Council of Hong Kong SAR under Projects: HKBU 2156/04E and HKBU 210306.
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Cheung, Ym., Zeng, H. (2007). Feature Weighted Rival Penalized EM for Gaussian Mixture Clustering: Automatic Feature and Model Selections in a Single Paradigm. In: Wang, Y., Cheung, Ym., Liu, H. (eds) Computational Intelligence and Security. CIS 2006. Lecture Notes in Computer Science(), vol 4456. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74377-4_107
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DOI: https://doi.org/10.1007/978-3-540-74377-4_107
Publisher Name: Springer, Berlin, Heidelberg
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