Deep Matrix Factorization

Deep Matrix Factorization

The multi-label annotation problem arises in situations such as object recognition in images where we want to automatically find the objects present in a given image. The solution consists in learning a classification model able to assign one or many labels to a particular sample. A method could be to learns a mapping between the features of the input sample and the labels, which is later used to predict labels for unannotated instances. The mapping between the feature representation and the labels is found by learning a common semantic representation using matrix factorization.


Jorge Vanegas (Ex-Miembro)


Vanegas J., Beltran V. and A. González F. (2015). Two-way Multimodal Online Matrix Factorization for Multi-label Annotation.  In Proceedings of the International Conference on Pattern Recognition Applications and Methods  (pp. 279-285). (PDF)