Learning the Structure for Structured Sparsity

Structured sparsity has recently emerged in statistics, machine learning and signal processing as a promising paradigm for learning in high-dimensional settings. All existing methods for learning under the assumption of structured sparsity rely on prior knowledge on how to weight (or how to penalize) individual subsets of variables during the subset selection process, which is not available in general. Inferring group weights from data is a key open research problem in structured sparsity.In this paper, we propose a Bayesian approach to the problem of group weight learning. We model the group weights as hyperparameters of heavy-tailed priors on groups of variables and derive an approximate inference scheme to infer these hyperparameters. We empirically show that we are able to recover the model hyperparameters when the data are generated from the model, and we demonstrate the utility of learning weights in synthetic and real denoising problems

The angular two-point correlation of NVSS galaxies revisited

We measure the angular two-point correlation and angular power spectrum from the NRAO VLA Sky Survey (NVSS) of radio galaxies. They are found to be consistent with the best-fit cosmological model from the Planck analysis, and with the redshift distribution obtained from the Combined EIS-NVSS Survey Of Radio Sources (CENSORS). Our analysis is based on an optimal estimation of the two-point correlation function and makes use of a new mask, that takes into account direction dependent effects of the observations, sidelobe effects of bright sources and galactic foreground. We also set a flux threshold and take the cosmic radio dipole into account. The latter turns out to be an essential step in the analysis. This improved cosmological analysis of the NVSS emphasizes the importance of a flux calibration that is robust and stable on large angular scales for future radio continuum surveys.Comment: 11 pages, 15 figure

Hydroelectric power plant management relying on neural networks and expert system integration

The use of Neural Networks (NN) is a novel approach that can help in taking decisions when integrated in a more general system, in particular with expert systems. In this paper, an architecture for the management of hydroelectric power plants is introduced. This relies on monitoring a large number of signals, representing the technical parameters of the real plant. The general architecture is composed of an Expert System and two NN modules: Acoustic Prediction (NNAP) and Predictive Maintenance (NNPM). The NNAP is based on Kohonen Learning Vector Quantization (LVQ) Networks in order to distinguish the sounds emitted by electricity-generating machine groups. The NNPM uses an ART-MAP to identify different situations from the plant state variables, in order to prevent future malfunctions. In addition, a special process to generate a complete training set has been designed for the ART-MAP module. This process has been developed to deal with the absence of data about abnormal plant situations, and is based on neural nets trained with the backpropagation algorithm.Publicad