کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8965188 | 1646702 | 2018 | 29 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
An improved locality preserving projection with â1-norm minimization for dimensionality reduction
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Locality preserving projection (LPP) is a classical tool for dimensionality reduction and feature extraction. It usually makes use of the â2-norm criterion for optimization, and is thus sensitive to outliers. In order to achieve robustness, LPP-L1 is proposed by employing the â1-norm as distance criterion. However, the edge weights of LPP-L1 measure only the dissimilarity of pairs of vertices and ignore the preservation of the similarity. In this paper, we develop a novel algorithm, termed as ILPP-L1, in which the â1-norm is utilized to obtain robustness and the similarities of pairs of vertices are effectively preserved, simultaneously. ILPP-L1 is robust to outliers because of the use of the â1-norm. The â1-norm minimization problem is directly solved, which ensures the preservation of the similarity of pairs of vertices. The solution is justified to converge to local minimum. In addition, ILPP-L1 avoids small sample size problem. Experiment results on benchmark databases confirm the effectiveness of the proposed method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 316, 17 November 2018, Pages 322-331
Journal: Neurocomputing - Volume 316, 17 November 2018, Pages 322-331
نویسندگان
Weizhong Yu, Rong Wang, Feiping Nie, Fei Wang, Qiang Yu, Xiaojun Yang,