کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10151189 | 1666107 | 2018 | 48 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Robust L1-norm multi-weight vector projection support vector machine with efficient algorithm
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
The recently proposed multi-weight vector projection support vector machine (EMVSVM) is an excellent multi-projections classifier. However, the formulation of MVSVM is based on the L2-norm criterion, which makes it prone to be affected by outliers. To alleviate this issue, in this paper, we propose a robust L1-norm MVSVM method, termed as MVSVML1. Specifically, our MVSVML1 aims to seek a pair of multiple projections such that, for each class, it maximizes the ratio of the L1-norm between-class dispersion and the L1-norm within-class dispersion. To optimize such L1-norm ratio problem, a simple but efficient iterative algorithm is further presented. The convergence of the algorithm is also analyzed theoretically. Extensive experimental results on both synthetic and real-world datasets confirm the feasibility and effectiveness of the proposed MVSVML1.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 315, 13 November 2018, Pages 345-361
Journal: Neurocomputing - Volume 315, 13 November 2018, Pages 345-361
نویسندگان
Wei-Jie Chen, Chun-Na Li, Yuan-Hai Shao, Ju Zhang, Nai-Yang Deng,