In this work, we propose sequential non-negative least squares (S-NNLS), an efficient algorithm for the solution of a sequence of non-negative least squares problem, which is more efficient than its batch counterparts when data are collected sequentially
International audienceWe propose a novel approach to solve exactly the sparse nonnega-tive least squ...
Nonnegative Matrix Approximation is an effective matrix decomposition technique that has proven to b...
Nonnegative matrix factorization (NMF) and nonnegative least squares regression (NNLS regression) ar...
algorithm for non-negative least squares (NNLS). Also allows the combination of non-negative and non...
We study the fundamental problem of nonnegative least squares. This problem was apparently introduce...
We study the fundamental problem of nonnegative least squares. This problem was apparently introduce...
We study the sparse non-negative least squares (S-NNLS) problem. S-NNLS occurs naturally in a wide v...
This document reviews the nonnegative least square (NNLS) optimisation problem and the usage of the ...
We present an efficient algorithm for large-scale non-negative least-squares (NNLS). We solve NNLS b...
Constrained least squares estimation lies at the heart of many applications in fields as diverse as ...
International audienceThe k-sparse nonnegative least squares (NNLS) problem is a variant of the stan...
National audienceThis article addresses least-squares minimization under sparsity and non-negativity...
We discuss a new simple method to solve linear programming (LP) problems, based on the so called dua...
Abstract—Due to the inherent physical characteristics of systems under investigation, non-negativity...
International audienceDue to the inherent physical characteristics of systems under investigation, n...
International audienceWe propose a novel approach to solve exactly the sparse nonnega-tive least squ...
Nonnegative Matrix Approximation is an effective matrix decomposition technique that has proven to b...
Nonnegative matrix factorization (NMF) and nonnegative least squares regression (NNLS regression) ar...
algorithm for non-negative least squares (NNLS). Also allows the combination of non-negative and non...
We study the fundamental problem of nonnegative least squares. This problem was apparently introduce...
We study the fundamental problem of nonnegative least squares. This problem was apparently introduce...
We study the sparse non-negative least squares (S-NNLS) problem. S-NNLS occurs naturally in a wide v...
This document reviews the nonnegative least square (NNLS) optimisation problem and the usage of the ...
We present an efficient algorithm for large-scale non-negative least-squares (NNLS). We solve NNLS b...
Constrained least squares estimation lies at the heart of many applications in fields as diverse as ...
International audienceThe k-sparse nonnegative least squares (NNLS) problem is a variant of the stan...
National audienceThis article addresses least-squares minimization under sparsity and non-negativity...
We discuss a new simple method to solve linear programming (LP) problems, based on the so called dua...
Abstract—Due to the inherent physical characteristics of systems under investigation, non-negativity...
International audienceDue to the inherent physical characteristics of systems under investigation, n...
International audienceWe propose a novel approach to solve exactly the sparse nonnega-tive least squ...
Nonnegative Matrix Approximation is an effective matrix decomposition technique that has proven to b...
Nonnegative matrix factorization (NMF) and nonnegative least squares regression (NNLS regression) ar...