Comment on “DSKmeans: A new kmeans-type approach to discriminative subspace clustering” by X. Huang et al. [Knowledge-Based Systems, Vol. 70, pp. 293-300, 2014]

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• 作者：Yahya Forghani ^{yahyafor2000@yahoo.com}
• 关键词：DSKmeans ; K-means ; Saddle point ; Indefinite
• 刊名：Knowledge-Based Systems
• 出版年：2017
• 出版时间：15 February 2017
• 年：2017
• 卷：118
• 期：Complete
• 页码：1-3
• 全文大小：472 K
• 卷排序：118

文摘

This paper comments on the published work dealing with "DSKmeans: A new kmeans-type approach to discriminative subspace clustering" [Knowledge-Based Systems, Vol. 70, pp. 293–300, 2014] proposed by X. Huang et al. Their clustering approach is based on a new mathematical model with two groups of variables: cluster centers and clusters memberships. They proposed an iterative algorithm to obtain the solution of this model. In each iteration, they fixed clusters memberships and claimed that the optimal cluster centers can be obtained by setting the derivative of the objective function of the model to zero. In this paper, we show that their proposed method cannot obtain the optimal solution of cluster centers for any given clusters memberships and some values of the model parameter.