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A Complete Subspace Analysis of Linear Discriminant Analysis and Its Robust Implementation
المؤلفون المشاركون
المصدر
Journal of Electrical and Computer Engineering
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-11-30
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
تكنولوجيا المعلومات وعلم الحاسوب
الملخص EN
Linear discriminant analysis has been widely studied in data mining and pattern recognition.
However, when performing the eigen-decomposition on the matrix pair (within-class scatter matrix and between-class scatter matrix) in some cases, one can find that there exist some degenerated eigenvalues, thereby resulting in indistinguishability of information from the eigen-subspace corresponding to some degenerated eigenvalue.
In order to address this problem, we revisit linear discriminant analysis in this paper and propose a stable and effective algorithm for linear discriminant analysis in terms of an optimization criterion.
By discussing the properties of the optimization criterion, we find that the eigenvectors in some eigen-subspaces may be indistinguishable if the degenerated eigenvalue occurs.
Inspired from the idea of the maximum margin criterion (MMC), we embed MMC into the eigen-subspace corresponding to the degenerated eigenvalue to exploit discriminability of the eigenvectors in the eigen-subspace.
Since the proposed algorithm can deal with the degenerated case of eigenvalues, it not only handles the small-sample-size problem but also enables us to select projection vectors from the null space of the between-class scatter matrix.
Extensive experiments on several face images and microarray data sets are conducted to evaluate the proposed algorithm in terms of the classification performance, and experimental results show that our method has smaller standard deviations than other methods in most cases.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Lu, Zhicheng& Liang, Zhizheng. 2016. A Complete Subspace Analysis of Linear Discriminant Analysis and Its Robust Implementation. Journal of Electrical and Computer Engineering،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1108431
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Lu, Zhicheng& Liang, Zhizheng. A Complete Subspace Analysis of Linear Discriminant Analysis and Its Robust Implementation. Journal of Electrical and Computer Engineering No. 2016 (2016), pp.1-10.
https://search.emarefa.net/detail/BIM-1108431
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Lu, Zhicheng& Liang, Zhizheng. A Complete Subspace Analysis of Linear Discriminant Analysis and Its Robust Implementation. Journal of Electrical and Computer Engineering. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1108431
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1108431
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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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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