Classification into normal and abnormal breast tissues using NMF and SVM

We present a methodology that uses Nonnegative Matrix Factorization (NMF) for feature extraction from mammogram images. These measures are used as input features for a Support Vector Machine classifier with the purpose of distinguishing tissues between normal and abnormal cases. We compared our results with another popular technique of matrix factorization called Independent Component Analysis (ICA). We obtained better results with NMF, that prove to be a competitive technique as feature extraction and analysis.