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

View Researcher II's Other Codes

Disclaimer: “The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).”

Please contact us in case of a broken link from here

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

Copyright Researcher II 2022