117 citations · 442 across the 44 of their papers we have counts for
50 papers
Hierarchical Clustering and Signal Denoising on Digraphs
Yi Wang, Sippanon Kitimoon, Hrushikesh N. Mhaskar +1
In this paper, we propose a representation of a digraph (directed graph) as a Hermitian matrix derived from its adjacency matrix. This representation characterizes both the connect…
Spectral Bounds for Kernel Quadrature
A. Cloninger, Q. T. Le Gia, H. N. Mhaskar
A bottleneck in the theory of kernel methods in machine learning is the storage requirement. To ameliorate this, a standard trick is to replace the kernel with an explicit feature…
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…
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…
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…
Aspects of a Generalized Theory of Sparsity based Inference in Linear Inverse Problems
Raghu G. Raj, Ryan O'Dowd, Owen T. Huber +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…