Unsupervised maximum margin feature selection via L2,1-norm minimization

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Authors Shizhun Yang, Chenping Hou, Feiping Nie, Yi Wu
Journal/Conference Name Neural Computing & Applications (NCA)
Paper Category
Paper Abstract In this article, we present an unsupervisedmaximum margin feature selection algorithm via sparseconstraints. The algorithm combines feature selection andK-means clustering into a coherent framework. L2,1-normregularization is performed to the transformation matrix toenable feature selection across all data samples. Ourmethod is equivalent to solving a convex optimizationproblem and is an iterative algorithm that converges to anoptimal solution. The convergence analysis of our algo-rithm is also provided. Experimental results demonstratethe ef´Čüciency of our algorithm.
Date of publication 2012
Code Programming Language MATLAB
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