Multi-View Local Learning

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-theart ones.

• Type: Conference
• Year: 2008
• Where: AAAI
• Authors: Dan Zhang, Fei Wang, Changshui Zhang, Tao Li
• Keywords: AAAI 2008, Intelligent Agents, Local Learning, Local Learning Methods, Multi-View Local Learning