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Nonparametric Feature Extraction via Direct Maximum Margin Alignment
Miao Cheng; Yuan Yan Tang; Chi-Man Pun
2011-12-01
Conference Name2011 10th International Conference on Machine Learning and Applications
Source PublicationProceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011
Volume1
Pages139-144
Conference Date18-21 Dec. 2011
Conference PlaceHonolulu, HI
CountryUSA
PublisherIEEE
Abstract

As an important step in machine learning and information processing, feature extraction has widely received attention in the past decades. For high-dimensional data, feature extraction problem usually comes down to exploit the intrinsic pattern information with dimensionality reduction. Since nonparametric approaches are applicable without parameter installation, they are more preferred in many real-world applications. In this work, a novel approach to direct maximum margin alignment (DMMA), is proposed for nonparametric feature reduction and extraction. Though there has been the straightforward solution for the discriminative ratio based subspace selection, such type of solution is still unavailable for maximum margin alignment. In terms of the kernel-view idea, DMMA can be performed by bringing in sample kernel, while the computational efficiency is achievable. Experiments on pattern recognition show that the proposed method is able to obtain comparable performance with several state-of-the-art algorithms. 

KeywordDimensionality Reduction Direct Maximum Margin Alignment Kernel-view Nonparametric Feature Extraction Subspace Selection
DOIhttps://doi.org/10.1109/ICMLA.2011.107
URLView the original
Indexed By其他
Language英语
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Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
AffiliationDepartment of Computer and Information Science University of Macau, Taipa, Macau
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Miao Cheng,Yuan Yan Tang,Chi-Man Pun. Nonparametric Feature Extraction via Direct Maximum Margin Alignment[C]:IEEE,2011:139-144.
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