Locality Versus Globality: Query-Driven Localized Linear Models for Facial Image Computing

Conventional subspace learning or recent feature extraction methods consider globality as the key criterion to design discriminative algorithms for image classification. We demonstrate in this paper that applying the local manner in sample space, feature space, and learning space via linear subspace learning can sufficiently boost the discriminating power, as measured by discriminating power coefficient (DPC). The proposed solution achieves good classification accuracy gains and shows computationally efficient. Particularly, we approximate the global nonlinearity through a multimodal localized piecewise subspace learning framework, in which three locality criteria can work individually or jointly for any new subspace learning algorithm design. It turns out that most existing subspace learning methods can be unified in such a common framework embodying either the global or local learning manner. On the other hand, we address the problem of numerical difficulty in the large-size pattern ...

• Type: Journal
• Year: 2008
• Where: TCSV
• Authors: Yun Fu, Zhu Li, Junsong Yuan, Ying Wu, Thomas S. Huang
• Keywords: Algorithm Design, Conventional Subspace, Localized Piecewise Subspace, TCSV 2008