کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6939141 | 1449969 | 2018 | 15 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Sparse Lq-norm least squares support vector machine with feature selection
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
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چکیده انگلیسی
Least squares support vector machine (LS-SVM) is a popular hyperplane-based classifier and has attracted many attentions. However, it may suffer from singularity or ill-condition issue for the small sample size (SSS) problem where the sample size is much smaller than the number of features of a data set. Feature selection is an effective way to solve this problem. Motivated by this, in the paper, we propose a sparse Lq-norm least squares support vector machine (Lq-norm LS-SVM) with 0â¯<â¯qâ¯<â¯1, where feature selection and prediction are performed simultaneously. Different from traditional LS-SVM, our Lq-norm LS-SVM minimizes the Lq-norm of weight and releases the least squares problem in primal space, resulting in that feature selection can be achieved effectively and small enough number of features can be selected by adjusting the parameters. Furthermore, our Lq-norm LS-SVM can be solved by an efficient iterative algorithm, which is proved to be convergent to a global optimal solution under some assumptions on the sparsity. The effectiveness of the proposed Lq-norm LS-SVM is validated via theoretical analysis as well as some illustrative numerical experiments.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 78, June 2018, Pages 167-181
Journal: Pattern Recognition - Volume 78, June 2018, Pages 167-181
نویسندگان
Yuan-Hai Shao, Chun-Na Li, Ming-Zeng Liu, Zhen Wang, Nai-Yang Deng,