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- Title
Semi-supervised adaptive feature analysis and its application for multimedia understanding.
- Authors
Yan, Fei; Zeng, Zhi-qiang; Hong, Chao-qun; Wang, Xiao-dong; Chen, Rung-Ching
- Abstract
Multimedia understanding for high dimensional data is still a challenging work, due to redundant features, noises and insufficient label information it contains. Graph-based semi-supervised feature learning is an effective approach to address this problem. Nevertheless, Existing graph-based semi-supervised methods usually depend on the pre-constructed Laplacian matrix but rarely modify it in the subsequent classification tasks. In this paper, an adaptive local manifold learning based semi-supervised feature selection is proposed. Compared to the state-of-the-art, the proposed algorithm has two advantages: 1) Adaptive local manifold learning and feature selection are integrated jointly into a single framework, where both the labeled and unlabeled data are utilized. Besides, the correlations between different components are also considered. 2) A group sparsity constraint, i.e. <italic>l</italic>2 , 1-norm, is imposed to select the most relevant features. We also apply the proposed algorithm to serval kinds of multimedia understanding applications. Experimental results demonstrate the effectiveness of the proposed algorithm.
- Subjects
FEATURE selection; SUPERVISED learning; INSTRUCTIONAL systems; ANNOTATIONS; LAPLACIAN matrices; HUMAN activity recognition
- Publication
Multimedia Tools & Applications, 2018, Vol 77, Issue 3, p3083
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-017-4990-5