On the convergence of the sparse possibilistic c-means algorithm
This addresses a theoretical gap for researchers in clustering and machine learning, but it is incremental as it builds on an existing algorithm.
The paper tackled the lack of a convergence proof for the sparse possibilistic c-means (SPCM) algorithm by providing a proof using the Zangwill convergence theorem, showing that the iterative sequence converges to a stationary point of the cost function.
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.