This paper presents a novel method to solve for the challenging problem of acoustic Room Impulse Response estimation (RIR). The approach formulates the RIR estimation as a Blind Channel Identification (BCI) problem and it exploits sparsity and non-negativity priors to reduce illposedness and to increase robustness of the solution to noise. This provides an iterative procedure based on a reweigh...

We describe ways to define and calculate L1-norm signal subspaces which are less sensitive to outlying data than L2-calculated subspaces. We focus on the computation of the L1 maximum-projection principal component of a data matrix containing N signal samples of dimension D and conclude that the general problem is formally NP-hard in asymptotically large N , D. We prove, however, that the case ...

A challenging problem in machine learning, information retrieval and computer vision research is how to recover a low-rank representation of the given data in the presence of outliers and missing entries. The L1-norm low-rank matrix factorization (LRMF) has been a popular approach to solving this problem. However, L1-norm LRMF is difficult to achieve due to its non-convexity and non-smoothness,...

In this paper, we develop a novel algorithm which finds a subset of Pareto front of a Multi-Objective Distributed Constraint Optimization Problem. This algorithm utilizes the Lp-norm method, pseudo-tree, and Dynamic Programming technique. Furthermore, we show that this Lp-norm based algorithm can only guarantee to find a Pareto optimal solution, when we employ L1-norm (Manhattan norm).

Various subspace clustering methods have benefited from introducing a graph regularisation term in their objective functions. In this work, we identify two critical limitations of the graph regularisation term employed in existing subspace clustering models and provide solutions for both of them. First, the squared l2-norm used in the existing term is replaced by a l1-norm term to make the regu...

In compressive sensing (CS) based inverse synthetic aperture radar (ISAR) imaging approaches, the quality of final image significantly depends on the number of measurements and the noise level. In this paper, we propose an improved version of CSbased method for inverse synthetic aperture radar (ISAR) imaging. Different from the traditional l1 norm based CS ISAR imaging method, our method explor...

We study the problem of recovering a sparse vector from a set of linear measurements. This problem also relates to feature or variable selection in statistics and machine learning. A widely used method for such problems has been regularization with the L1 norm. We extend this methodology to allow for a broader class of regularizers which includes the L1 norm. This class is characterized by a co...

For many human machine interaction systems, techniques for continuously estimating the vigilance of operators are highly desirable to ensure work safety. Up to now, various signals are studied for vigilance analysis. Among them, electroencephalogram (EEG) is the most commonly used signal. In this paper, extreme learning machine (ELM) and its modifications with L1 norm and L2 norm penalties are ...

Aims. In this study, we compare several methods of modeling large-scale systematic differences between catalogs positions extragalactic radio sources provided by very long baseline interferometry with an emphasis on mitigating the impact outliers. Methods. The coordinate difference was parameterized first and second order coefficients vector spherical harmonics. We solved for these using least-...

Suppose we have a signal y which we wish to represent using a linear combination of a number of basis atoms ai, y = ∑ i xiai = Ax. The problem of finding the minimum l0 norm representation for y is a hard problem. The Basis Pursuit (BP) approach proposes to find the minimum l1 norm representation instead, which corresponds to a linear program (LP) that can be solved using modern LP techniques, ...