Flexible Manifold Learning with Optimal Graph for Image and Video Representation

Wei Wang, Yan Yan, Feiping Nie, Shuicheng Yan, Nicu Sebe

科研成果: 期刊稿件文章同行评审

49 引用 (Scopus)

摘要

Graph-based dimensionality reduction techniques have been widely and successfully applied to clustering and classification tasks. The basis of these algorithms is the constructed graph which dictates their performance. In general, the graph is defined by the input affinity matrix. However, the affinity matrix derived from the data is sometimes suboptimal for dimension reduction as the data used are very noisy. To address this issue, we propose the projective unsupervised flexible embedding models with optimal graph (PUFE-OG). We build an optimal graph by adjusting the affinity matrix. To tackle the out-of-sample problem, we employ a linear regression term to learn a projection matrix. The optimal graph and the projection matrix are jointly learned by integrating the manifold regularizer and regression residual into a unified model. The experimental results on the public benchmark datasets demonstrate that the proposed PUFE-OG outperforms state-of-the-art methods.

源语言英语
页(从-至)2664-2675
页数12
期刊IEEE Transactions on Image Processing
27
6
DOI
出版状态已出版 - 6月 2018

指纹

探究 'Flexible Manifold Learning with Optimal Graph for Image and Video Representation' 的科研主题。它们共同构成独一无二的指纹。

引用此