Diverse Non-Negative Matrix Factorization for Multiview Data Representation

Position-Aware Non-negative Matrix Factorization for Satellite Image Representation

Satellite images clustering is a challenging problem in remote sensing and machine vision, where each image content is represented by a high-dimensional feature vector. However, the feature vectors might not be appropriate to express the semantic content of images, which eventually leads to poor results in clustering and classification. To tackle this problem, we propose a novel approach to gen...

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

Non-negative Matrix Factorization (NMF) is a part-based image representation method. It comes from the intuitive idea that entire face image can be constructed by combining several parts. In this paper, we propose a framework for face recognition by finding localized, part-based representations, denoted “Iterative weighted non-smooth non-negative matrix factorization” (IWNS-NMF). A new cost fun...

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...

Norm and Hessian Regularized Non-Negative Matrix Factorization with Discriminability for Data Representation

Matrix factorization based methods have widely been used in data representation. Among them, Non-negative Matrix Factorization (NMF) is a promising technique owing to its psychological and physiological interpretation of spontaneously occurring data. On one hand, although traditional Laplacian regularization can enhance the performance of NMF, it still suffers from the problem of its weak extra...