Multi-View Local Learning

Dan Zhang, Fei Wang, Changshui Zhang, Tao Li

The idea of local learning, i.e., classifying a particular example based on its neighbors, has been successfully applied to many semi-supervised and clustering problems recently. However, the local learning methods developed so far are all devised for single-view problems. In fact, in many real-world applications, examples are represented by multiple sets of features. In this paper, we extend the idea of local learning to multi-view problem, design a multi-view local model for each example, and propose a Multi-View Local Learning Regularization (MVLL-Reg) matrix. Both its linear and kernel version are given. Experiments are conducted to demonstrate the superiority of the proposed method over several state-of-the-art ones.

Subjects: 12. Machine Learning and Discovery; Please choose a second document classification

Submitted: Apr 14, 2008


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