Efficacy of Non-negative Matrix Factorization for Feature Selection in Cancer Data

Dropout Non-negative Matrix Factorization for Independent Feature Learning

Non-negative Matrix Factorization (NMF) can learn interpretable parts-based representations of natural data, and is widely applied in data mining and machine learning area. However, NMF does not always achieve good performances as the non-negative constraint leads learned features to be non-orthogonal and overlap in semantics. How to improve the semantic independence of latent features without ...

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...

Regularized Non-Negative Matrix Factorization for Dynamic and Relational Data

Data involving repeated measurements of several variables over different factors, experimental conditions or time may exhibit correlations among variables, as well as between factors. The discovery of these underlying, meaningful relations is important to a wide variety of areas such as psychology, signal processing, finance, among others. Common methods such as independent component analysis, ...