We give an efficient algorithm for finding sparse approximate solutions to linear systems of equations with nonnegative coefficients. Unlike most known results for sparse recovery, we do not require any assumption on the matrix other than non-negativity. Our algorithm is combinatorial in nature, inspired by techniques for the set cover problem, as well as the multiplicative weight update method...

Nonnegative Matrix Factorization (NMF) is a data analysis technique which allows compression and interpretation of nonnegative data. NMF became widely studied after the publication of the seminal paper by Lee and Seung (Learning the Parts of Objects by Nonnegative Matrix Factorization, Nature, 1999, vol. 401, pp. 788–791), which introduced an algorithm based on Multiplicative Updates (MU). More...

In this paper, a class of high-ordered neural networks are investigated. By rigorous analysis, a set of sufficient conditions ensuring the existence of a nonnegative periodic solution and its R +-asymptotical stability are established. The results obtained can also be applied to the first-ordered neural networks.

this paper presents a modified digital image watermarking method based on nonnegative matrix factorization. firstly, host image is factorized to the product of three nonnegative matrices. then, the centric matrix is transferred to discrete cosine transform domain. watermark is embedded in low frequency band of this matrix and next, the reverse of the transform is computed. finally, watermarked ...