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
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Robust L1-norm multi-weight vector projection support vector machine with efficient algorithm
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
نویسندگان
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