A Generalized Discriminative Least Squares Regression Model
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DOI码：10.1109/ACPR.2017.3
发表刊物：PROCEEDINGS 2017 4TH IAPR ASIAN CONFERENCE ON PATTERN RECOGNITION (ACPR)
关键字：FACE RECOGNITION; CLASSIFICATION
摘要：Least squares regression (LSR) is a fundamental tool in statistics theory. In this paper, we propose a generalized discriminative least squares regression (GDLSR) model for multicategory classification. The main motivation of GDLSR is to introduce a translation matrix to enhance the flexibility of the target matrix. Through adding the graph constraint into the translation matrix, GDLSR can make the samples in the same class have similar translation vectors. To optimize our proposed GDLSR, an efficient iteration algorithm is proposed to find the global optimal solution. Extensive experiments results on face data sets confirm the effectiveness of GDLSR.
合写作者：Weiyang Li,Fangyuan Xu,Loi Lei Lai,Houqing Zheng,Junjie Zheng,Zhimin Wang
第一作者：Haoliang Yuan
论文类型：会议论文
文献类型：J
ISSN号：2327-0985
是否译文：否
发表时间：2017-11-26
收录刊物：SCI