Constrained Concept Factorization for Image Representation

Matrix factorization based techniques, such as nonnegative matrix factorization and concept factorization, have attracted great attention in dimensionality reduction and data clustering. Previous studies show that both of them yield impressive results on image processing and document clustering. However, both of them are essentially unsupervised methods and cannot incorporate label information. In this paper, we propose a novel semi-supervised matrix decomposition method for extracting the image concepts that are consistent with the known label information. With this constraint, we call the new approach constrained concept factorization. By requiring that the data points sharing the same label have the same coordinate in the new representation space, this approach has more discriminating power. The experimental results on several corpora show good performance of our novel algorithm in terms of clustering accuracy and mutual information.