Iterative Weighted Non-smooth Non-negative Matrix Factorization for Face Recognition

LDA-based Non-negative Matrix Factorization for Supervised Face Recognition

In PCA based face recognition, the basis images may contain negative pixels and thus do not facilitate physical interpretation. Recently, the technique of nonnegative matrix Factorization (NMF) has been applied to face recognition: the non-negativity constraint of NMF yields a localized parts-based representation which achieves a recognition rate that is on par with the eigenface approach. In t...

Non-negative matrix factorization framework for face recognition

Non-negative Matrix Factorization (NMF) is a part-based image representation method which adds a non-negativity constraint to matrix factorization. NMF is compatible with the intuitive notion of combining parts to form a whole face. In this paper, we propose a framework of face recognition by adding NMF constraint and classifier constraints to matrix factorization to get both intuitive features...

A Weighted Non-Negative Matrix Factorization for Local Representations

This paper presents an improvement of the classical Non-negative Matrix Factorization (NMF) approach, for dealing with local representations of image objects. NMF, when applied to global data representations such as faces presents a high ability to represent local features of the original data in an unsupervised way. However, when applied to local representations NMF generates redundant basis. ...

Non-negative Matrix Factorization for Face Illumination Analysis

Changing illumination causes severe problems for face recognition in uncontrolled environments. It might be helpful for illumination invariant face recognition if information about the illumination can be recovered from the given face image. In this paper an illumination classification method based on Non-negative Matrix Factorization(NMF) is proposed. The traditional NMF approach together with...

Non-negative matrix factorization

A new approach for building recommender system using non negative matrix factorization method

Nonnegative Matrix Factorization is a new approach to reduce data dimensions. In this method, by applying the nonnegativity of the matrix data, the matrix is ​​decomposed into components that are more interrelated and divide the data into sections where the data in these sections have a specific relationship. In this paper, we use the nonnegative matrix factorization to decompose the user ratin...