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Indexed by:会议论文

Date of Publication:2014-12-14

Included Journals:EI、CPCI-S、Scopus

Volume:2015-January

Issue:January

Page Number:74-79

Key Words:pattern recognition; proximal classifier; sparse learning; absolute value inequalities; linear program

Abstract:In this paper, we propose a robust proximal classifier via absolute value inequalities (AVIPC) for pattern classification. AVIPC determines K proximal planes by solving K optimization problems with absolute value inequalities. In AVIPC, each proximal plane is closer to one class and far away from the others. By using the absolute value inequalities, AVIPC is more robust and sparse than traditional proximal classifiers. The optimization problems can be solved by an iterative algorithm, and its convergence has been proved. Preliminary experimental results on visual and public available datasets show the comparable performance and stability of the proposed method.