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International audienceAn increasing number of applications is concerned with recovering a sparse matrix from noisy observations. In this paper, we consider the setting where each row of the unknown matrix is sparse. We establish minimax optimal rates of convergence for estimating matrices with row sparsity. A major focus in the present paper is on the derivation of lower bounds
A vector or matrix is said to be sparse if the number of non-zero elements is significantly smaller ...
Given a matrix, the seriation problem consists in permuting its rows in such way that all its column...
We study sparse principal components analysis in high dimensions, where p (the number of variables) ...
International audienceAn increasing number of applications is concerned with recovering a sparse mat...
This paper considers estimation of sparse covariance matrices and establishes the optimal rate of co...
Precision matrix is of significant importance in a wide range of applications in multivariate analys...
International audienceThis paper considers the problem of recovery of a low-rank matrix in the situa...
The use of sparsity has emerged in the last fifteen years as an important tool for solving many prob...
This paper focusses on the sparse estimation in the situation where both the the sens-ing matrix and...
This paper considers a sparse spiked covariance matrix model in the high-dimensional setting and stu...
The paper deals with the estimation of the maximal sparsity degree for which a given measurement mat...
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/2...
Abstract The aim of this paper is to develop strategies to estimate the sparsity degree of a signal ...
We analyze a class of estimators based on a convex relaxation for solving high-dimensional matrix de...
For the Gaussian sequence model, we obtain non-asymp-totic minimax rates of estimation of the linear...
A vector or matrix is said to be sparse if the number of non-zero elements is significantly smaller ...
Given a matrix, the seriation problem consists in permuting its rows in such way that all its column...
We study sparse principal components analysis in high dimensions, where p (the number of variables) ...
International audienceAn increasing number of applications is concerned with recovering a sparse mat...
This paper considers estimation of sparse covariance matrices and establishes the optimal rate of co...
Precision matrix is of significant importance in a wide range of applications in multivariate analys...
International audienceThis paper considers the problem of recovery of a low-rank matrix in the situa...
The use of sparsity has emerged in the last fifteen years as an important tool for solving many prob...
This paper focusses on the sparse estimation in the situation where both the the sens-ing matrix and...
This paper considers a sparse spiked covariance matrix model in the high-dimensional setting and stu...
The paper deals with the estimation of the maximal sparsity degree for which a given measurement mat...
This work was also published as a Rice University thesis/dissertation: http://hdl.handle.net/1911/2...
Abstract The aim of this paper is to develop strategies to estimate the sparsity degree of a signal ...
We analyze a class of estimators based on a convex relaxation for solving high-dimensional matrix de...
For the Gaussian sequence model, we obtain non-asymp-totic minimax rates of estimation of the linear...
A vector or matrix is said to be sparse if the number of non-zero elements is significantly smaller ...
Given a matrix, the seriation problem consists in permuting its rows in such way that all its column...
We study sparse principal components analysis in high dimensions, where p (the number of variables) ...
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