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» Model Selection Through Sparse Maximum Likelihood Estimation
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ICASSP
2011
IEEE
12 years 11 months ago
Maximum a posteriori based regularization parameter selection
The 1 norm regularized least square technique has been proposed as an efficient method to calculate sparse solutions. However, the choice of the regularization parameter is still...
Ashkan Panahi, Mats Viberg
ICML
2009
IEEE
14 years 8 months ago
Sparse Gaussian graphical models with unknown block structure
Recent work has shown that one can learn the structure of Gaussian Graphical Models by imposing an L1 penalty on the precision matrix, and then using efficient convex optimization...
Benjamin M. Marlin, Kevin P. Murphy
SDM
2009
SIAM
202views Data Mining» more  SDM 2009»
14 years 4 months ago
Proximity-Based Anomaly Detection Using Sparse Structure Learning.
We consider the task of performing anomaly detection in highly noisy multivariate data. In many applications involving real-valued time-series data, such as physical sensor data a...
Tsuyoshi Idé, Aurelie C. Lozano, Naoki Abe,...
ECCV
2002
Springer
14 years 9 months ago
Multimodal Data Representations with Parameterized Local Structures
Abstract. In many vision problems, the observed data lies in a nonlinear manifold in a high-dimensional space. This paper presents a generic modelling scheme to characterize the no...
Ying Zhu, Dorin Comaniciu, Stuart C. Schwartz, Vis...
ICANN
2010
Springer
13 years 8 months ago
A Directional Laplacian Density for Underdetermined Audio Source Separation
In this work, a novel probability distribution is proposed to model sparse directional data. The Directional Laplacian Distribution (DLD) is a hybrid between the linear Laplacian d...
Nikolaos Mitianoudis