Discriminant basis for object classification

This paper presents a technique to obtain a discriminant basis set in an unsupervised way. A non-negative matrix factorization (NMF) is applied over a set of color newspapers to obtain a reduced space considering only positive constraints. This method is compared with the well-known principal component analysis (PCA), obtaining promising results in the task of representing independent behaviors of the input data. With this methodology, we are able to find an ordered list of the basis functions, with it being possible to select some of them for a further discriminant task. Moreover the method can also be applied to the task of automatically extracting object classes from a set of objects.