Add to ORKGAbstract We survey our latest results on the development and analysis of adaptive approximation algorithms for sparse data representation, where special emphasis is placed on the Easy Path Wavelet Transform (EPWT), nonlinear dimensionality reduction (NDR) methods, and their application to signal separation and detection.
This paper gives a precise characterization of the fundamental limits of adaptive sensing for divers...
AbstractEnormous progress has been made in the construction and analysis of adaptive wavelet methods...
In this paper we describe and analyze an algorithm for the fast computation of sparse wavelet coecie...
Abstract. This course gives an introduction to the design of efficient datatypes for adaptive wavele...
A central problem in approximation theory is the concise representation of functions. Given a functi...
The sparse coding is approximation/representation of signals with the minimum number of coefficients...
A multi-step adaptive resampling procedure is proposed, and shown to be an effective approach when d...
The goal of the sparse approximation problem is to approximate a target signal using a linear combin...
A vector or matrix is said to be sparse if the number of non-zero elements is significantly smaller ...
The goal of this thesis is to demonstrate practical application of sparse data representation in the...
In this paper, we propose the use of (adaptive) nonlinear ap-proximation for dimensionality reductio...
Abstract—Finding sparse approximate solutions to large under-determined linear systems of equations ...
The purpose of this paper is to present an adaptive algorithm to find the best approximation in the ...
Sparse representation has attracted much attention from researchers in fields of signal processing, ...
We consider large–scale scattered data problems where the information is given in form of nonuniform...
This paper gives a precise characterization of the fundamental limits of adaptive sensing for divers...
AbstractEnormous progress has been made in the construction and analysis of adaptive wavelet methods...
In this paper we describe and analyze an algorithm for the fast computation of sparse wavelet coecie...
Abstract. This course gives an introduction to the design of efficient datatypes for adaptive wavele...
A central problem in approximation theory is the concise representation of functions. Given a functi...
The sparse coding is approximation/representation of signals with the minimum number of coefficients...
A multi-step adaptive resampling procedure is proposed, and shown to be an effective approach when d...
The goal of the sparse approximation problem is to approximate a target signal using a linear combin...
A vector or matrix is said to be sparse if the number of non-zero elements is significantly smaller ...
The goal of this thesis is to demonstrate practical application of sparse data representation in the...
In this paper, we propose the use of (adaptive) nonlinear ap-proximation for dimensionality reductio...
Abstract—Finding sparse approximate solutions to large under-determined linear systems of equations ...
The purpose of this paper is to present an adaptive algorithm to find the best approximation in the ...
Sparse representation has attracted much attention from researchers in fields of signal processing, ...
We consider large–scale scattered data problems where the information is given in form of nonuniform...
This paper gives a precise characterization of the fundamental limits of adaptive sensing for divers...
AbstractEnormous progress has been made in the construction and analysis of adaptive wavelet methods...
In this paper we describe and analyze an algorithm for the fast computation of sparse wavelet coecie...