Non-negative matrix factorization based unmixing for principal component transformed hyperspectral data

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...

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...

Learning quantifiable associations via principal sparse non-negative matrix factorization

Association rules are traditionally designed to capture statistical relationship among itemsets in a given database. To additionally capture the quantitative association knowledge, Korn et.al. recently propose a paradigm named Ratio Rules [6] for quantifiable data mining. However, their approach is mainly based on Principle Component Analysis (PCA), and as a result, it cannot guarantee that the...

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