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Self-Weighted Adaptive Locality Discriminant Analysis
Guo, Muhan1; Nie, Feiping1; Li, Xuelong2
Conference Name25th IEEE International Conference on Image Processing, ICIP 2018
Source Publication2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
Conference Date2018-10-07
Conference PlaceAthens, Greece
PublisherIEEE Computer Society
Contribution Rank2
AbstractThe linear discriminant analysis (LDA) is a popular technique for dimensionality reduction, nevertheless, when the input data lie in a complicated geometry distribution, LDA tends to obtain undesired results since it neglects the local structure of data. Though plenty of previous works devote to capturing the local structure, they have the same weakness that the neighbors found in the original data space may be not reliable, especially when noise is large. In this paper, we propose a novel supervised dimensionality reduction approach, Self-weighted Adaptive Locality Discriminant Analysis (SALDA), which aims to find a representative low-dimensional subspace of data. Compared with LDA and its variants, SALDA explores the neighborhood relationship of data points in the desired subspace effectively. Besides, the weights between within-class data points are learned automatically without setting any additional parameter. Extensive experiments on synthetic and real-world datasets show the effectiveness of the proposed method. © 2018 IEEE.
Indexed ByEI
EI Accession Number20191206646594
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Document Type会议论文
Affiliation1.School of Computer Science, Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, Xi'an, Shaanxi; 710072, China;
2.Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'An Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, Shaanxi; 710119, China
Recommended Citation
GB/T 7714
Guo, Muhan,Nie, Feiping,Li, Xuelong. Self-Weighted Adaptive Locality Discriminant Analysis[C]:IEEE Computer Society,2018:3378-3382.
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