Hidden-concept driven image decomposition towards semi-supervised multi-label image annotation
Proceedings of the First International Conference on Internet Multimedia Computing and Service
Proceedings of the ACM International Conference on Image and Video Retrieval
Image label completion by pursuing contextual decomposability
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Multi-class learning from class proportions
Neurocomputing
Joint Laplacian feature weights learning
Pattern Recognition
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In this paper, we restudy the non-convex data factorization problems (regularized or not, unsupervised or supervised), where the optimization is confined in the \emph{nonnegative} orthant, and provide a \emph{unified} convergency provable solution based on multiplicative nonnegative update rules. This solution is general for optimization problems with block-wisely quadratic objective functions, and thus direct update rules can be derived by skipping over the tedious specific procedure deduction process and algorithmic convergence proof. By taking this unified solution as a general template, we i) re-explain several existing nonnegative data factorization algorithms, ii) develop a variant of nonnegative matrix factorization formulation for handling out-of-sample data, and iii) propose a new nonnegative data factorization algorithm, called Correlated Co-Decomposition (CCD), to simultaneously factorize two feature spaces by exploring the inter-correlated information. Experiments on both face recognition and multi-label image annotation tasks demonstrate the wide applicability of the unified solution as well as the effectiveness of two proposed new algorithms.