Hessian regularization by patch alignment framework

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• 作者：Weifeng Liu^aAuthor Vitae ; Hongli Liu^aAuthor Vitae ; Dapeng Tao^b ; ^{dapeng.tao@gmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Semi-supervised learning ; Hessian ; Patch alignment ; Least squares
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：5 September 2016
• 年：2016
• 卷：204
• 期：Complete
• 页码：183-188
• 全文大小：1612 K

文摘

In recent years, semi-supervised learning has played a key part in large-scale image management, where usually only a few images are labeled. To address this problem, many representative works have been reported, including transductive SVM, universum SVM, co-training and graph-based methods. The prominent method is the patch alignment framework, which unifies the traditional spectral analysis methods. In this paper, we propose Hessian regression based on the patch alignment framework. In particular, we construct a Hessian using the patch alignment framework and apply it to regression problems. To the best of our knowledge, there is no report on Hessian construction from the patch alignment viewpoint. Compared with the traditional Laplacian regularization, Hessian can better match the data and then leverage the performance. To validate the effectiveness of the proposed method, we conduct human face recognition experiments on a celebrity face dataset. The experimental results demonstrate the superiority of the proposed solution in human face classification.