The problem of uncovering transcriptional regulation by transcription factors (TFs) based on microarray data is considered. A novel Bayesian sparse correlated rectified factor mod...
The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maxim...
Abstract—We investigate the scalability of the hypergraphbased sparse matrix partitioning methods with respect to the increasing sizes of matrices and number of nonzeros. We prop...
This paper deals with the Bayesian signal denoising problem, assuming a prior based on a sparse representation modeling over a unitary dictionary. It is well known that the maximum...
The representation model that considers an image as a sparse linear combination of few atoms of a predefined or learned dictionary has received considerable attention in recent ye...