On the convergence of the sparse possibilistic c-means algorithm

2015/08/05 by Spyridoula D. Xenaki, Xenaki, Spyridoula D., Konstantinos Koutroumbas +3
Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #Blind Source Separation Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Sparse and Compressive Sensing Techniques

paper · pdf · doi:10.48550/arxiv.1508.01057

openalex publication_date 2015/08/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

In this paper, a convergence proof for the recently proposed sparse possibilistic c-means (SPCM) algorithm is provided, utilizing the celebrated Zangwill convergence theorem. It is shown that the iterative sequence generated by SPCM converges to a stationary point or there exists a subsequence of it that converges to a stationary point of the cost function of the algorithm.

Citations

• Convergence theory for fuzzy c-means: Counterexamples and repairs

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