Multi-Label Classification by Semi-Supervised Singular Value Decomposition

Liping Jing, Chenyang Shen, Liu Yang, Jian Yu, Michael K. Ng

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


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.

Original languageEnglish (US)
Article number7959114
Pages (from-to)4612-4625
Number of pages14
JournalIEEE Transactions on Image Processing
Issue number10
StatePublished - Oct 2017
Externally publishedYes


  • Image classification
  • manifold regularization
  • multi-label
  • nuclear norm regularization
  • singular value decomposition

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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