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Neural Computing and Applications

Erschienen in:

11.07.2018 | Original Article

A discriminant graph nonnegative matrix factorization approach to computer vision

verfasst von: Xiangguang Dai, Guo Chen, Chuandong Li

Erschienen in: Neural Computing and Applications | Ausgabe 11/2019

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Abstract

This paper proposes a novel dimensional reduction method, called discriminant graph nonnegative matrix factorization (DGNMF), for image representation. Inspired by manifold learning and linear discrimination analysis, DGNMF provides a compact representation which can respect the original data space. In addition, In addition, the within-class distance of each class in the representation is very small. Based on these characteristics, our proposed method can be viewed as a supervised learning method, which outperforms some existing dimensional reduction methods, including PCA, LPP, LDA, NMF and GNMF. Experiments on image recognition have shown that our approach can provide a better representation than some classic methods.

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Titel: A discriminant graph nonnegative matrix factorization approach to computer vision
verfasst von: Xiangguang Dai
Guo Chen
Chuandong Li
Publikationsdatum: 11.07.2018
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 11/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-3608-9

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