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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| Veröffentlicht in: | Neurocomputing (Amsterdam) 2014-09, Vol.139, p.180-188 |
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| container_title | Neurocomputing (Amsterdam) |
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| creator | Huang, Pu Chen, Caikou Tang, Zhenmin Yang, Zhangjing |
| description | 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. |
| doi_str_mv | 10.1016/j.neucom.2014.02.047 |
| format | Article |
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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.</description><identifier>ISSN: 0925-2312</identifier><identifier>EISSN: 1872-8286</identifier><identifier>DOI: 10.1016/j.neucom.2014.02.047</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>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. 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| ispartof | Neurocomputing (Amsterdam), 2014-09, Vol.139, p.180-188 |
| issn | 0925-2312 1872-8286 |
| language | eng |
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| source | Elsevier ScienceDirect Journals |
| 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 |
| title | Discriminant similarity and variance preserving projection for feature extraction |
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