Non-negative matrix factorization based methods for object recognition

摘要

Non-negative matrix factorization (NMF) is a new feature extraction method. But the learned feature vectors are not directly suitable for further analysis such as object recognition using the nearest neighbor classifier in contrast to traditional principal component analysis (PCA) because the learned bases are not orthonormal to each other. This paper investigates how to improve the accuracy of recognition based on this new method from two viewpoints. One is to adopt a Riemannian metric like distance for the learned feature vectors instead of Euclidean distance. The other is to first orthonormalize the learned bases and then to use the projections of data based on the orthonormalized bases for further recognition. Experiments on the USPS database demonstrate the proposed methods can improve accuracy and even outperform PCA. We believe that the proposed methods can make NMF used as widely as PCA.