Asymptotic Generalization Bound of Fisher’s Linear Discriminant Analysis
نویسندگان
چکیده
منابع مشابه
Asymptotic Generalization Bound of Fisher's Linear Discriminant Analysis
Fisher's linear discriminant analysis (FLDA) is an important dimension reduction method in statistical pattern recognition. It has been shown that FLDA is asymptotically Bayes optimal under the homoscedastic Gaussian assumption. However, this classical result has the following two major limitations: 1) it holds only for a fixed dimensionality D, and thus does not apply when D and the training s...
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0167-8655/$ see front matter 2013 Elsevier B.V. A http://dx.doi.org/10.1016/j.patrec.2013.01.016 ⇑ Corresponding author. Tel.: +82 (0) 31 219 2480; E-mail addresses: [email protected] (J.H. Oh) @ieee.org (N. Kwak). 1 Jae Hyun Oh is pursuing a Ph.D. degree at the Computer Engineering, Ajou University, Republic of Ko 2 Nojun Kwak is an associate professor at the Depart Engineering, Ajou Universi...
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Fisher Linear Discriminant Analysis (also called Linear Discriminant Analysis(LDA)) are methods used in statistics, pattern recognition and machine learning to find a linear combination of features which characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later c...
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ژورنال
عنوان ژورنال: IEEE Transactions on Pattern Analysis and Machine Intelligence
سال: 2014
ISSN: 0162-8828,2160-9292
DOI: 10.1109/tpami.2014.2327983