The new emerging theory of compressive sampling demonstrates that by exploiting the structure of a signal, it is possible to sample a signal below the Nyquist rate—using random projections—and achieve perfect reconstruction. In this paper, we consider a special case of compressive sampling where the uncompressed signal is non-negative, and propose a number of sparse recovery algorithms—which ut...

Non-negative matrix factorization and sparse representation models have been successfully applied in high-throughput biological data analysis. In this paper, we propose our versatile sparse matrix factorization (VSMF) model for biological data mining. We show that many well-known sparse models are specific cases of VSMF. Through tuning parameters, sparsity, smoothness, and non-negativity can be...

We obtain new lower bounds on the number of non zeros of sparse poly-nomials and give a fully polynomial time (;) approximation algorithm for the number of non-zeros of multivariate sparse polynomials over a nite eld of q elements and degree less than q ? 1. This answers partially to an open problem of D. Grigoriev and M. Karpinski. Also, probabilistic and determin-istic algorithms for testing ...

Domain decomposition methods are widely used to solve sparse linear systems from scientific problems, but they are not suited to solve sparse linear systems extracted from integrated circuits. The reason is that the sparse linear system of integrated circuits may be non-diagonal-dominant, and domain decomposition method might be unconvergent for these non-diagonal-dominant matrices. In this pap...

A variety of representation learning approaches have been investigated for reinforcement learning; much less attention, however, has been given to investigating the utility of sparse coding. Outside of reinforcement learning, sparse coding representations have been widely used, with non-convex objectives that result in discriminative representations. In this work, we develop a supervised sparse...

In sparse representation problem, there is always interest to reduce the solution space by introducing additional constraints. This can lead efficient application-specific algorithms. Despite known advantages of sparsity and non-negativity for image data representation, limited studies have addressed these characteristics simultaneously, due challenges involved. this paper, we propose a novel i...