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Robust Structure Preserving Nonnegative Matrix Factorization for Dimensionality Reduction
المؤلفون المشاركون
Han, Zhi
Tang, Yandong
Li, Bingfeng
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-06-29
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
As a linear dimensionality reduction method, nonnegative matrix factorization (NMF) has been widely used in many fields, such as machine learning and data mining.
However, there are still two major drawbacks for NMF: (a) NMF can only perform semantic factorization in Euclidean space, and it fails to discover the intrinsic geometrical structure of high-dimensional data distribution.
(b) NMF suffers from noisy data, which are commonly encountered in real-world applications.
To address these issues, in this paper, we present a new robust structure preserving nonnegative matrix factorization (RSPNMF) framework.
In RSPNMF, a local affinity graph and a distant repulsion graph are constructed to encode the geometrical information, and noisy data influence is alleviated by characterizing the data reconstruction term of NMF with l 2,1 -norm instead of l 2 -norm.
With incorporation of the local and distant structure preservation regularization term into the robust NMF framework, our algorithm can discover a low-dimensional embedding subspace with the nature of structure preservation.
RSPNMF is formulated as an optimization problem and solved by an effective iterative multiplicative update algorithm.
Experimental results on some facial image datasets clustering show significant performance improvement of RSPNMF in comparison with the state-of-the-art algorithms.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Li, Bingfeng& Tang, Yandong& Han, Zhi. 2016. Robust Structure Preserving Nonnegative Matrix Factorization for Dimensionality Reduction. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1112583
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Li, Bingfeng…[et al.]. Robust Structure Preserving Nonnegative Matrix Factorization for Dimensionality Reduction. Mathematical Problems in Engineering No. 2016 (2016), pp.1-14.
https://search.emarefa.net/detail/BIM-1112583
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Li, Bingfeng& Tang, Yandong& Han, Zhi. Robust Structure Preserving Nonnegative Matrix Factorization for Dimensionality Reduction. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1112583
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1112583
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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