Improved Collaborative Non-Negative Matrix Factorization and Total Variation for Hyperspectral Unmixing

Unmixing of Hyperspectral Images using Bayesian Non-negative Matrix Factorization with Volume Prior

Hyperspectral imaging can be used in assessing the quality of foods by decomposing the image into constituents such as protein, starch, and water. Observed data can be considered a mixture of underlying characteristic spectra (endmembers), and estimating the constituents and their abundances requires efficient algorithms for spectral unmixing. We present a Bayesian spectral unmixing algorithm e...

Nonnegative Matrix Factorization With Data-Guided Constraints For Hyperspectral Unmixing

Abstract: Hyperspectral unmixing aims to estimate a set of endmembers and corresponding abundances in pixels. Nonnegative matrix factorization (NMF) and its extensions with various constraints have been widely applied to hyperspectral unmixing. L1/2 and L2 regularizers can be added to NMF to enforce sparseness and evenness, respectively. In practice, a region in a hyperspectral image may posses...

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...

An Improved Non-negative Matrix Factorization Method for Masquerade Detection

A local-knowledge method for masquerade detection that uses a Non-negative Matrix Factorization (NMF) algorithm is here proposed. This method does not consider training data from other users to build a specific user profile but his own. It is used a normalization phase that helps improve a previous NMF-based method by Wang et.al. Comparisons with other local-knowledge methods like Wang’s, Hidde...

Non-negative matrix factorization