期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 26, 期 10, 页码 4612-4625出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2017.2719939
关键词
Image classification; singular value decomposition; multi-label; nuclear norm regularization; manifold regularization
资金
- NSFC [61375062]
- Ph.D. Programs Foundation of Ministry of Education of China [20120009110006]
- PCSIRT [IRT201206]
- HKRGC [GRFs HKBU12306616, HKBU12302715, CRF C1007-15G]
Multi-label problems arise in various domains, including automatic multimedia data categorization, and have generated significant interest in computer vision and machine learning community. However, existing methods do not adequately address two key challenges: exploiting correlations between labels and making up for the lack of labelled data or even missing labelled data. In this paper, we proposed to use a semi-supervised singular value decomposition (SVD) to handle these two challenges. The proposed model takes advantage of the nuclear norm regularization on the SVD to effectively capture the label correlations. Meanwhile, it introduces manifold regularization on mapping to capture the intrinsic structure among data, which provides a good way to reduce the required labelled data with improving the classification performance. Furthermore, we designed an efficient algorithm to solve the proposed model based on the alternating direction method of multipliers, and thus, it can efficiently deal with large-scale data sets. Experimental results for synthetic and real-world multimedia data sets demonstrate that the proposed method can exploit the label correlations and obtain promising and better label prediction results than the state-of-the-art methods.
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