Unsupervised maximum margin feature selection via L2,1-norm minimization
By: Researcher II
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| Authors | Shizhun Yang, Chenping Hou, Feiping Nie, Yi Wu |
| Journal/Conference Name | Neural Computing & Applications (NCA) |
| Paper Category | Computer Science |
| 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 efficiency of our algorithm. |
| Date of publication | 2012 |
| Code Programming Language | MATLAB |
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