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A computational and theoretical analysis of local null space discriminant method for pattern classification
Cheng M.; Fang B.; Tang Y.Y.; Chen H.
2011-02-01
Source PublicationInternational Journal of Pattern Recognition and Artificial Intelligence
ISSN02180014
Volume25Issue:1Pages:117-134
AbstractMany problems in pattern classification and feature extraction involve dimensionality reduction as a necessary processing. Traditional manifold learning algorithms, such as ISOMAP, LLE, and Laplacian Eigenmap, seek the low-dimensional manifold in an unsupervised way, while the local discriminant analysis methods identify the underlying supervised submanifold structures. In addition, it has been well-known that the intraclass null subspace contains the most discriminative information if the original data exist in a high-dimensional space. In this paper, we seek for the local null space in accordance with the null space LDA (NLDA) approach and reveal that its computational expense mainly depends on the quantity of connected edges in graphs, which may be still unacceptable if a great deal of samples are involved. To address this limitation, an improved local null space algorithm is proposed to employ the penalty subspace to approximate the local discriminant subspace. Compared with the traditional approach, the proposed method can achieve more efficiency so that the overload problem is avoided, while slight discriminant power is lost theoretically. A comparative study on classification shows that the performance of the approximative algorithm is quite close to the genuine one. © 2011 World Scientific Publishing Company.
Keyworddimensionality reduction local discriminant analysis local null space manifold learning Pattern classification
DOI10.1142/S0218001411008476
URLView the original
Language英語
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
AffiliationChongqing University
Recommended Citation
GB/T 7714
Cheng M.,Fang B.,Tang Y.Y.,et al. A computational and theoretical analysis of local null space discriminant method for pattern classification[J]. International Journal of Pattern Recognition and Artificial Intelligence,2011,25(1):117-134.
APA Cheng M.,Fang B.,Tang Y.Y.,&Chen H..(2011).A computational and theoretical analysis of local null space discriminant method for pattern classification.International Journal of Pattern Recognition and Artificial Intelligence,25(1),117-134.
MLA Cheng M.,et al."A computational and theoretical analysis of local null space discriminant method for pattern classification".International Journal of Pattern Recognition and Artificial Intelligence 25.1(2011):117-134.
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