In this work, we propose a novel supervised matrix factorization method used directly as a multi-class classifier. The coefficient matrix of the factorization is enforced to be sparse by ℓ 1 -norm regularization. The basis matrix is composed of atom dictionaries from different classes, which are trained in a jointly supervised manner by penalizing inhomogeneous representations given the labeled data samples. The learned basis matrix models the data of interest as a union of discriminative linear subspaces by sparse projection. The proposed model is based on the observation that many high-dimensional natural signals lie in a much lower dimensional subspaces or union of subspaces. Experiments conducted on several datasets show the effectiveness of such a representation model for classification, which also suggests that a tight reconstructive representation model could be very useful for discriminant analysis.
In REF , Yang et al. proposed supervised matrix factorization method used directly as multiclass classifier.
Learning the sparse representation for classification
2011 IEEE International Conference on Multimedia and Expo
2011 IEEE International Conference on Multimedia and Expo
Computer Science