Discriminative orthogonal elastic preserving projections for classification

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• 作者：Tingjin Luo^a ; ^{luotingjin03@nudt.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Chenping Hou^a ; ^{hcpnudt@hotmail.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Dongyun Yi^a ; ^{dongyunyi@sina.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Jun Zhang^b ; ^{aiya0215@263.net" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Machine learning ; Manifold learning ; Linear dimensionality reduction ; Orthogonal analysis ; Discriminative orthogonal elastic preserving projections (DOEPP)
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：29 February 2016
• 年：2016
• 卷：179
• 期：Complete
• 页码：54-68
• 全文大小：7275 K

文摘

The traditional manifold learning methods can preserve the local sub-manifold structure or the global geometry effectively, such as elastic preserving projections (EPP). Many experimental results have been shown that EPP, a recently developed linear algorithm, is a strong analyzer for high-dimensional data. However, for classification problems, the traditional methods focused on the geometrical information and ignores discriminative information of different classes. In this paper, we propose a novel discriminative orthogonal elastic preserving projections (DOEPP) by imposing the discriminant information and the orthogonal constraint to improve its classification performance. DOEPP does not only preserve the elasticity of the training set, but also sufficiently utilizes the discriminant information by adding maximum margin criterion and the orthogonality of the projection matrix into its objective function. Extensive experiments on two well-known synthetic manifold data sets and four publicly available databases illustrate the effectiveness of our method.