8 papers
Localized kernel method for separation of linear chirps
Eric Mason, Sippanon Kitimoon, Hrushikesh Mhaskar
The task of separating a superposition of signals into its individual components is a common challenge encountered in various signal processing applications, especially in domains…
A signal separation view of classification
H. N. Mhaskar, Ryan O'Dowd
The problem of classification in machine learning has often been approached in terms of function approximation. In this paper, we propose an alternative approach for classification…
An Approximation Theory Perspective on Machine Learning
Hrushikesh N. Mhaskar, Efstratios Tsoukanis, Ameya D. Jagtap
A central problem in machine learning is often formulated as follows: Given a dataset , which is a sample drawn from an unknown probability distribution, th…
Robust and tractable multidimensional exponential analysis
H. N. Mhaskar, S. Kitimoon, Raghu G. Raj
Motivated by a number of applications in signal processing, we study the following question. Given samples of a multidimensional signal of the form $$ f(\boldsymbol\ell)=\sum_{k=1}…
An Eigenfunction Approach to Conversion of the Laplace Transform of Point Masses on the Real Line to the Fourier Domain
Michael McKenna, Hrushikesh N. Mhaskar, Richard G. Spencer
Motivated by applications in magnetic resonance relaxometry, we consider the following problem: Given samples of a function , where …
Aspects of a Generalized Theory of Sparsity based Inference in Linear Inverse Problems
Ryan O'Dowd, Raghu G. Raj, Hrushikesh N. Mhaskar +1
Linear inverse problems are ubiquitous in various science and engineering disciplines. Of particular importance in the past few decades, is the incorporation of sparsity based prio…