Add to ORKGIn this note, we develop fast and deterministic dimensionality reduction techniques for a family of subspace approximation problems. Let P ⊂ RN be a given set of M points. The techniques developed herein find an O(n logM)-dimensional subspace that is guaranteed to always contain a near-best fit n-dimensional hyperplane H for P with respect to the cumulative projection error x∈P ‖x−ΠHx‖p
This paper presents a fast algorithm for robust subspace recovery. The datasets considered include p...
Model-order reduction methods tackle the following general approximation problem: find an "easily-co...
Reduced bases have been introduced for the approximation of parametrized PDEs in applications where ...
In the subspace approximation problem, given m points in R^{n} and an integer k <= n, the goal is to...
We consider the problem of approximating a set P of n points in Rd by a j-dimensional subspace under...
© 2019 Elsevier B.V. Dimension reduction is often an important step in the analysis of high-dimensio...
International audienceWe consider the following fitting problem: given an arbitrary set of N points ...
In this article we consider the Data Projection Method (DPM), which constitutes a simple and reliabl...
A standard engineering procedure for approximating the solutions of an infinite-dimensional inverse ...
In this article we consider the Data Projection Method (DPM), which constitutes a simple and reliabl...
We show that the problem of finding the simplex of largest volume in the convex hull of n points in ...
© 2018 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserv...
We propose algorithms for constructing linear embeddings of a finite dataset V ⊂ ℝ[superscript d] in...
The problem of efficiently deciding which of a database of models is most similar to a given input q...
International audienceThis paper is concerned with the approximation of a function $u$ in a given su...
This paper presents a fast algorithm for robust subspace recovery. The datasets considered include p...
Model-order reduction methods tackle the following general approximation problem: find an "easily-co...
Reduced bases have been introduced for the approximation of parametrized PDEs in applications where ...
In the subspace approximation problem, given m points in R^{n} and an integer k <= n, the goal is to...
We consider the problem of approximating a set P of n points in Rd by a j-dimensional subspace under...
© 2019 Elsevier B.V. Dimension reduction is often an important step in the analysis of high-dimensio...
International audienceWe consider the following fitting problem: given an arbitrary set of N points ...
In this article we consider the Data Projection Method (DPM), which constitutes a simple and reliabl...
A standard engineering procedure for approximating the solutions of an infinite-dimensional inverse ...
In this article we consider the Data Projection Method (DPM), which constitutes a simple and reliabl...
We show that the problem of finding the simplex of largest volume in the convex hull of n points in ...
© 2018 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserv...
We propose algorithms for constructing linear embeddings of a finite dataset V ⊂ ℝ[superscript d] in...
The problem of efficiently deciding which of a database of models is most similar to a given input q...
International audienceThis paper is concerned with the approximation of a function $u$ in a given su...
This paper presents a fast algorithm for robust subspace recovery. The datasets considered include p...
Model-order reduction methods tackle the following general approximation problem: find an "easily-co...
Reduced bases have been introduced for the approximation of parametrized PDEs in applications where ...