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3 January 2020 Self-weighted locality discriminative feature selection
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Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 113730U (2020)
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China
Linear discriminant analysis (LDA) is one of the most popular methods for dimensionality reduction, and there have many different variants based on LDA. However, conventional LDA have some drawbacks: 1) The projection matrix offers the disadvantage of interpretability; 2) LDA assumes that every class data are drawn from Gaussian distribution which may not be applicable to many real-world data. Aiming to solve these problems, we propose a robust feature selection method, namely Self-weighted Locality Discriminative Feature Selection (SLD-FS), by combining row sparsity l2,1-norm regularization and self-weighted locality discriminant analysis strategy. Additionally, an effective iterative algorithm is developed to optimise this objective function, and the algorithm is proved to convergence. Extensive experiments conducted on various data sets demonstrate the effectiveness of SLD-FS when compared with some state-of-the-art supervised feature selection methods.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Haifeng Zhao, Bowen Zhang, Shaojie Zhang, and Jian Zhang "Self-weighted locality discriminative feature selection", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113730U (3 January 2020);

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