In order to improve the performance of Least Mean Square (LMS) based system identification of sparse systems, a new adaptive algorithm is proposed which utilizes the sparsity property of such systems. A general approximating approach on l0 norm – a typical metric of system sparsity, is proposed and integrated into the cost function of the LMS algorithm. This integration is equivalent to add a z...

Spatially adaptive nonparametric regression estimation is one of the most promising recent directions in image processing. The Transforms and Spectral Techniques Research Group at the Department of Signal Processing, Tampere University of Technology, has been active in this novel …eld starting from about 2002. The results achieved with application to di¤erent image and video processing problems...

We propose real-time scan-matching based on L0norm minimization under dynamic crowded environment. The prior scan-matching methods are based on L2-norm minimization, because the measurement noise follows the normal distribution in static environments. This assumption is unfortunately broken in dynamic crowded environments. We propose to use the idea of Locality Sensitive Hashing (LSH) to accele...

A new reweighted proportionate affine projection algorithm (RPAPA) with memory and row action projection (MRAP) is proposed in this paper. The reweighted PAPA is derived from a family of sparseness measures, which demonstrate performance similar to mu-law and the l0 norm PAPA but with lower computational complexity. The sparseness of the channel is taken into account to improve the performance ...

We consider a class of l0-minimization problems, which is to search for the partial sparsest solution to an underdetermined linear system with additional constraints. We introduce several concepts, including lp-induced norm (0 < p < 1), maximal scaled spark and scaled mutual coherence, to develop several new uniqueness conditions for the partial sparsest solution to this class of l0-minimizatio...

Robust compressive sensing(CS) reconstruction has become an attractive research topic in recent years. Robust CS aims to reconstruct the sparse signals under non-Gaussian(i.e. heavy tailed) noises where traditional CS reconstruction algorithms may perform very poorly due to utilizing l2 norm of the residual vector in optimization. Most of existing robust CS reconstruction algorithms are based o...

Sparsity driven signal processing has gained tremendous popularity in the last decade. At its core, the assumption is that the signal of interest is sparse with respect to either a fixed transformation or a signal dependent dictionary. To better capture the data characteristics, various dictionary learning methods have been proposed for both reconstruction and classification tasks. For classifi...

In the medical computer tomography (CT) field, total variation (TV), which is the 1  -norm of the discrete gradient transform (DGT), is widely used as regularization based on the comprehensive sensing (CS) theory. To overcome the TV model’s disadvantageous tendency of uniformly penalize the image gradient and over smooth the low-contrast structures, an iterative algorithm based on the 0  -nor...

Learning the “blocking” structure is a central challenge for high dimensional data (e.g., gene expression data). In [Lee et al., 2010], a sparse singular value decomposition (SVD) has been used as a biclustering tool to achieve this goal. However, this model ignores the structural information between variables (e.g., gene interaction graph). Although typical graph-regularized norm can incorpora...

In this paper, we propose a method to address the problem of source estimation for Sparse Component Analysis (SCA) in the presence of additive noise. Our method is a generalization of a recently proposed method (SL0), which has the advantage of directly minimizing the ℓ 0-norm instead of ℓ 1-norm, while being very fast. SL0 is based on minimization of the smoothed ℓ 0-norm subject to As = x. In...