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Indexed by:会议论文

Date of Publication:2018-01-01

Included Journals:CPCI-S

Page Number:252-257

Key Words:classification; hypergraph; multi-modal; adaptive weights

Abstract:Image classification is one of the most important fundamental research topics in machine learning and image processing. Recently, hypergraph learning, which can model the high-order relationship of samples and fusion multimodal features, has received the attention of many researchers. However, existing multimodal hypergraph learning methods face two problems, i.e., how to construct hyperedges and how to determine the weights of hyperedges. This paper proposes an adaptive multimodal hypergraph learning method (AMH) to address these two challenges. AMH uses multiple neighborhoods method to avoid generating a k-uniform hyperedge, and optimizes the weights with the penalty function method to take the initial labels into consideration. The experimental results demonstrate the effectiveness of AMH compared with the stateof-the-art methods.