@inproceedings{5dd2c5a139e0487b8ed1c82641a556f8,
title = "Schatten-p norm based linear regression discriminant analysis for face recognition",
abstract = "Locality-regularized linear regression classification (LLRC) shows good performance on face recognition. However, it sorely performs on the original space, which results in degraded classification efficiency. To solve this problem, we propose a dimensionality reduction algorithm named schatten-p norm based linear regression discriminant analysis (SPLRDA) for image feature extraction. First, it defines intra-class and inter-class scatters based on schatten-p norm, which improves the capability to deal with illumination changes. Then the objective function which incorporates discriminant analysis is derived from the minimization of intra-class compactness and the maximization of inter-class separability. Experiments carried on some typical databases validate the effectiveness and robustness of our method.",
keywords = "Dimensionality reduction, Discriminant analysis, Face recognition, Feature extraction, Linear regression, Schatten-P norm",
author = "Lijiang Chen and Wentao Dou and Xia Mao",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd., 2018.; 13th Conference on Image and Graphics Technologies and Applications, IGTA 2018 ; Conference date: 08-04-2018 Through 10-04-2018",
year = "2018",
doi = "10.1007/978-981-13-1702-6\_5",
language = "英语",
isbn = "9789811317019",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "45--56",
editor = "Yongtian Wang and Yuxin Peng and Zhiguo Jiang",
booktitle = "Image and Graphics Technologies and Applications - 13th Conference on Image and Graphics Technologies and Applications, IGTA 2018, Revised Selected Papers",
address = "德国",
}