论文名称：Synthesis linear classifier based analysis dictionary learning for pattern classification
论文类型：期刊论文
发表刊物：NEUROCOMPUTING
收录刊物：SCIE、EI、Scopus
卷号：238
页面范围：103-113
ISSN号：0925-2312
关键字：Analysis dictionary learning; Synthesis linear classifier; Pattern classification
摘要：Dictionary learning approaches have been widely applied to solve pattern classification problems and have achieved promising performance. However, most of works aim to learn a discriminative synthesis dictionary and sparse coding coefficients for classification. Until recent years, analysis dictionary learning began to attract interest from researchers. In this paper, we present a novel discriminative analysis dictionary learning frame, named Synthesis Linear Classifier based Analysis Dictionary Learning (SLC-ADL). Firstly, we incorporate a synthesis-linear-classifier-based error term into the basic analysis dictionary learning model, whose classification performance is obviously improved by making full use of the label information. Then, we develop an alternating iterative algorithm to solve the new model and obtain closed-form solutions leading to pretty competitive running efficiency. What is more, we design three classification schemes by fully exploiting the synthesis linear classifier. Finally, extensive comparison experiments on scene categorization, object classification, action recognition and face recognition clearly verify the classification performance of the proposed algorithm. (C) 2017 Elsevier B.V. All rights reserved.
发表时间：2017-05-17