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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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.

论文关键词:DSKmeans,K-means,Saddle point,Indefinite

论文评审过程:Received 10 August 2016, Revised 28 September 2016, Accepted 1 November 2016, Available online 2 November 2016, Version of Record 12 January 2017.

论文官网地址:https://doi.org/10.1016/j.knosys.2016.11.002