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New Blind Free Bands Detectors using Cyclic Autocorrelation Function Sparsity
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Sparse approximations and inverse problems in acoustics
In this work, we construct sparse approximations of acoustic fields, as well as methods for solving inverse problems based on these approximations. The approximation... -
On the Use of the Sparse Property of the Cyclic Autocorrelation Function to B...
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Local stability and robustness of sparse dictionary learning in the presence ...
A popular approach within the signal processing and machine learning communities consists in modelling signals as sparse linear combinations of atoms selected from a... -
Robust Sparse Analysis Regularization
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BLasso for object categorization and retrieval: Towards interpretable visual ...
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Local Behavior of Sparse Analysis Regularization: Applications to Risk Estima...
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Statistical Learning for Resting-State fMRI: Successes and Challenges
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Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regress...
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The degrees of freedom of the Group Lasso for a General Design
In this paper, we are concerned with regression problems where covariates can be grouped in nonoverlapping blocks, and where only a few of them are assumed to be... -
Proximal Splitting Derivatives for Risk Estimation
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Structured Sparsity-Inducing Norms : Statistical and Algorithmic Properties w...
Numerous fields of applied sciences and industries have been witnessing a process of digitisation over the past few years. This trend has come with a steady increase... -
Sparsity-Promoting Bayesian Dynamic Linear Models
Sparsity-promoting priors have become increasingly popular over recent years due to an increased number of regression and classification applications involving a large... -
Machine Learning Methods for Visual Object Detection
The goal of this thesis is to develop better practical methods for detecting common object classes in real world images. We present a family of object detectors that... -
X-ray CT Image Reconstruction from Few Projections
To improve the safety (lower dose) and the productivity (faster acquisition) of an X-ray CT system, we want to reconstruct a high quality image from a small number of... -
Analysis of a Random Forests Model
Random forests are a scheme proposed by Leo Breiman in the 2000's for building a predictor ensemble with a set of decision trees that grow in randomly selected... -
Exploiting Sparse Property of the Cyclic Autocorrelation Function for Cyclost...
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Sparsity-based optimization of two lifting-based wavelet transforms for semi-...
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Compressible Distributions for High-dimensional Statistics
Was previously entitled "Compressible priors for high-dimensional statistics"
