Non-negative Matrix Factorization and Its Extensions for Spectral Image Data Analysis

Nonnegative Matrix Factorization for Spectral Data Analysis

Data analysis is pervasive throughout business, engineering and science. Very often the data to be analyzed is nonnegative, and it is often preferable to take this constraint into account in the analysis process. Here we are concerned with the application of analyzing data obtained using astronomical spectrometers, which provide spectral data which is inherently nonnegative. The identification ...

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...

Inferential, robust non-negative matrix factorization analysis of microarray data

MOTIVATION Modern methods such as microarrays, proteomics and metabolomics often produce datasets where there are many more predictor variables than observations. Research in these areas is often exploratory; even so, there is interest in statistical methods that accurately point to effects that are likely to replicate. Correlations among predictors are used to improve the statistical analysis....

Non-negative matrix factorization

Analyzing Non-Negative Matrix Factorization for Image Classification

The Non-negative Matrix Factorization technique (NMF) has been recently proposed for dimensionality reduction. NMF is capable to produce a regionor partbased representation of objects and images. This paper experimentally compares NMF to Principal Component Analysis (PCA) in the context of image patch classification. A first finding is that the two techniques are complementary and that their re...