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A DCA Based Algorithm for Feature Selection in Model-Based Clustering

  • Viet Anh Nguyen
  • Hoai An Le Thi
  • Hoai Minh LeEmail author
Conference paper
  • 314 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12033)

Abstract

Gaussian Mixture Models (GMM) is a model-based clustering approach which has been used in many applications thanks to its flexibility and effectiveness. However, in high dimension data, GMM based clustering lost its advantages due to over-parameterization and noise features. To deal with this issue, we incorporate feature selection into GMM clustering. For the first time, a non-convex sparse inducing regularization is considered for feature selection in GMM clustering. The resulting optimization problem is nonconvex for which we develop a DCA (Difference of Convex functions Algorithm) to solve. Numerical experiments on several benchmark and synthetic datasets illustrate the efficiency of our algorithm and its superiority over an EM method for solving the GMM clustering using \(l_1\) regularization.

Keywords

Model-based clustering Gaussian Mixture Models Variable selection Non-convex regularization DC programming DCA 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer Science and Application Department, LGIPMUniversity of LorraineMetzFrance

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