Alternating Direction Method of Multipliers for L 1 - and L 2 -norm Best Fitting Hyperplane Classifier | Zendy

Chen Wang | Zendy; ChunNa Li | Zendy; Huaxin Pei | Zendy; Yanru Guo | Zendy; YuanHai Shao | Zendy

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Alternating Direction Method of Multipliers for L 1 - and L 2 -norm Best Fitting Hyperplane Classifier

Author(s) -

Chen Wang,

ChunNa Li,

Huaxin Pei,

Yanru Guo,

YuanHai Shao

Publication year - 2017

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2017.05.049

Subject(s) - hyperplane , computer science , classifier (uml) , outlier , regular polygon , norm (philosophy) , mathematical optimization , algorithm , artificial intelligence , mathematics , combinatorics , geometry , political science , law

Recently, two-sided best fitting hyperplane classifier (2S-BFHC) is proposed, which has several significant advantages over previous proximal hyperplane classifiers. Moreover, Concave-Convex Procedure (CCCP) has already been provided to solve the dual problem of 2S-BFHC. In this paper, we solve the primal problem of 2S-BFHC by the alternating direction method of multipliers (ADMM) which is well suited to solve the distributed optimization problem, and we also propose a robust L 1 -norm two-sided best fitting hyperplane classifier ( L 1 -2S-BFHC) with ADMM, which aims at giving a robust performance for the problem with outliers. Priliminary numerical results demonstrate the effectiveness of proposed methods.

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