Maximum likelihood-based gridless doa estimation using structured covariance matrix recovery and sbl with grid refinement
We consider the parametric measurement model employed in applications such as line
spectral or direction-of-arrival estimation with the goal to estimate the underlying parameter …
spectral or direction-of-arrival estimation with the goal to estimate the underlying parameter …
A distributed spatio-temporal EEG/MEG inverse solver
W Ou, MS Hämäläinen, P Golland - NeuroImage, 2009 - Elsevier
We propose a novel ℓ1ℓ2-norm inverse solver for estimating the sources of EEG/MEG
signals. Based on the standard ℓ1-norm inverse solvers, this sparse distributed inverse …
signals. Based on the standard ℓ1-norm inverse solvers, this sparse distributed inverse …
Multimodal multipart learning for action recognition in depth videos
The articulated and complex nature of human actions makes the task of action recognition
difficult. One approach to handle this complexity is dividing it to the kinetics of body parts and …
difficult. One approach to handle this complexity is dividing it to the kinetics of body parts and …
On the uniqueness of overcomplete dictionaries, and a practical way to retrieve them
A full-rank under-determined linear system of equations Ax= b has in general infinitely many
possible solutions. In recent years there is a growing interest in the sparsest solution of this …
possible solutions. In recent years there is a growing interest in the sparsest solution of this …
Atomic decomposition and sparse representation for complex signal analysis in machinery fault diagnosis: A review with examples
Complex signal analysis is a key topic in machinery fault diagnosis. For complex multi-
component signals of various morphological contents, the commonly used basis expansion …
component signals of various morphological contents, the commonly used basis expansion …
Multi-channel linear prediction-based speech dereverberation with sparse priors
A Jukić, T van Waterschoot… - … /ACM Transactions on …, 2015 - ieeexplore.ieee.org
The quality of speech signals recorded in an enclosure can be severely degraded by room
reverberation. In this paper, we focus on a class of blind batch methods for speech …
reverberation. In this paper, we focus on a class of blind batch methods for speech …
A comparison of typical ℓp minimization algorithms
Recently, compressed sensing has been widely applied to various areas such as signal
processing, machine learning, and pattern recognition. To find the sparse representation of …
processing, machine learning, and pattern recognition. To find the sparse representation of …
Iterative reweighted minimization methods for regularized unconstrained nonlinear programming
Z Lu - Mathematical Programming, 2014 - Springer
In this paper we study general l_p lp regularized unconstrained minimization problems. In
particular, we derive lower bounds for nonzero entries of the first-and second-order …
particular, we derive lower bounds for nonzero entries of the first-and second-order …
Sparse adaptive filters for echo cancellation
Adaptive filters with a large number of coefficients are usually involved in both network and
acoustic echo cancellation. Consequently, it is important to improve the convergence rate …
acoustic echo cancellation. Consequently, it is important to improve the convergence rate …
Family of iterative LS-based dictionary learning algorithms, ILS-DLA, for sparse signal representation
K Engan, K Skretting, JH Husøy - Digital Signal Processing, 2007 - Elsevier
The use of overcomplete dictionaries, or frames, for sparse signal representation has been
given considerable attention in recent years. The major challenges are good algorithms for …
given considerable attention in recent years. The major challenges are good algorithms for …