Dispersion constraint based non-negative sparse coding algorithm

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• 作者：Li Shang^a ; ^{shangli0930@126.com" class="auth_mail" title="E-mail the corresponding author} ; ^{sl0930@jssvc.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Xin Wang^b ; ^{xwa49@sfu.ca" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Zhan-Li Sun^c ; ^{zhlsun2006@yahoo.com.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Non-negative sparse coding (NNSC) ; Sparse coding (SC) ; Feature extraction ; Image reconstruction ; Feature classification
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：5 May 2016
• 年：2016
• 卷：188
• 期：Complete
• 页码：253-261
• 全文大小：1035 K

文摘

The basic Non-negation Sparse Coding (NNSC) algorithm behaves both advantages of basic Sparse Coding (SC) and Non-negative Matrix Factorization (NMF). It has been proved to be quite more reasonable than SC in explaining the receptive fields of simple cells in V1 in the primary visual system of mammals, and also suitable to process the larger and high dimension data. However, it ignores class information constraint of data, and when it is used in classification task, the advantage is not outstanding. To get better class efficiency, considering the within-class and between class dispersion constraint rule, a new NNSC algorithm based on dispersion constraint denoted by DCB-NNSC one is put forward in this paper, which combines the feature discriminability constraint supervised by classification task and the maximized sparseness criteria in the cost function. At the same time, to ensure the self-adaptive sparse measurement, the Normal Inverse Gaussian Density (NIG) density model is used as sparse penalty function of features. In test, the property of feature extraction is first discussed by implementing the image reconstruction task by using features extracted, and then, the feature recognition capability is further explored by using several classifiers. In the test, test images are provided by the PolyU database. Simulation results show that DCB-NNSC algorithm can capture some significant receptive fields with clearer sparsity and image structure, which are not only efficient in extracting image features, but also favor well the image classification task. Furthermore, comparing DCB-NNSC algorithm discussed here with basic NNSC, experimental results show that DCB-NNSC algorithm is indeed effective and has a good potential in the research task of feature extraction and classification for pattern recognition.