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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2018

05.05.2017

Discriminant Analysis with Local Gaussian Similarity Preserving for Feature Extraction

verfasst von: Xi Liu, Zhengming Ma

Erschienen in: Neural Processing Letters | Ausgabe 1/2018

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Abstract

In this paper, we propose a novel discriminant analysis with local Gaussian similarity preserving (DA-LGSP) method for feature extraction. DA-LGSP can be viewed as a linear approximation of manifold learning based approach which seeks to find a linear projection that maximizes the between-class dissimilarities under the constraint of locality preserving. The local geometry of each point is preserved by the Gaussian coefficients of its neighbors, meanwhile the between-class dissimilarities are represented by Euclidean distances. Experiments are conducted on USPA data, COIL-20 dataset, ORL dataset and FERET dataset. The performance of the proposed method demonstrates that DA-LGSP is effective in feature extraction.
Vorheriger Artikel A Feature Selection Method for Projection Twin Support Vector Machine
Nächster Artikel Almost Periodic Solutions for SICNNs with Neutral Type Proportional Delays and D Operators

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Metadaten
Titel
Discriminant Analysis with Local Gaussian Similarity Preserving for Feature Extraction
verfasst von
Xi Liu
Zhengming Ma
Publikationsdatum
05.05.2017
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2018
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-017-9630-6

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