Discriminant similarity and variance preserving projection for feature extraction

In this paper, a novel supervised dimensionality reduction algorithm called discriminant similarity and variance preserving projection (DSVPP) is presented for feature extraction and recognition. More specifically, we redefine the intrinsic graph and penalty graph to model the intra-class compactnes...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 139; pp. 180 - 188
Main Authors Huang, Pu, Chen, Caikou, Tang, Zhenmin, Yang, Zhangjing
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 02.09.2014
Elsevier
Subjects
Applied sciences
Artificial intelligence
Computer science; control theory; systems
Data points
Data processing. List processing. Character string processing
Dimensionality reduction
Exact sciences and technology
Feature extraction
Graphs
Intrinsic graph
Manifold-based learning
Memory organisation. Data processing
Pattern recognition. Digital image processing. Computational geometry
Penalty graph
Preserving
Projection
Scatter
Similarity
Software
Variance
Dimensionality reduction
Intrinsic graph
Penalty graph
Manifold-based learning
Variance
Separability
Computer vision
Discriminant analysis
Face recognition
Compactness
Graph theory
Pattern recognition
Manifold learning
Data mining
Dimension reduction
Experimental result
Multidimensional analysis
Selection criterion
Feature extraction
Reduced order model
Pattern extraction
Multiple classification
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Summary:In this paper, a novel supervised dimensionality reduction algorithm called discriminant similarity and variance preserving projection (DSVPP) is presented for feature extraction and recognition. More specifically, we redefine the intrinsic graph and penalty graph to model the intra-class compactness and inter-class separability of data points, where the intrinsic graph characterizes the similarity information of the same-class points and the penalty graph characterizes the variance information of the not-same-class points. Using the two graphs, the within-class scatter and the between-class scatter are computed, and then a concise feature extraction criterion is raised via minimizing the difference between them. Experimental results on the Wine data set, ORL, FERET and AR face databases show the effectiveness of the proposed method.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2014.02.047