5 citations · 6 across the 5 of their papers we have counts for
10 papers
Efficient randomized tensor-based algorithms for function approximation and low-rank kernel interactions
Arvind K. Saibaba, Rachel Minster, Misha E. Kilmer
In this paper, we introduce a method for multivariate function approximation using function evaluations, Chebyshev polynomials, and tensor-based compression techniques via the Tuck…
Structured Matrix Approximations via Tensor Decompositions
Misha E. Kilmer, Arvind K. Saibaba
We provide a computational framework for approximating a class of structured matrices; here, the term structure is very general, and may refer to a regular sparsity pattern (e.g.,…
A survey of subspace recycling iterative methods
Kirk M. Soodhalter, Eric de Sturler, Misha Kilmer
This survey concerns subspace recycling methods, a popular class of iterative methods that enable effective reuse of subspace information in order to speed up convergence and find…
Tensor-Tensor Products for Optimal Representation and Compression
Misha Kilmer, Lior Horesh, Haim Avron +1
In this era of big data, data analytics and machine learning, it is imperative to find ways to compress large data sets such that intrinsic features necessary for subsequent analys…
An Inner-Outer Iterative Method for Edge Preservation in Image Restoration and Reconstruction
Silvia Gazzola, Misha E. Kilmer, James G. Nagy +2
We present a new inner-outer iterative algorithm for edge enhancement in imaging problems. At each outer iteration, we formulate a Tikhonov-regularized problem where the penalizati…
Non-negative Tensor Patch Dictionary Approaches for Image Compression and Deblurring Applications
Elizabeth Newman, Misha E. Kilmer
In recent work (Soltani, Kilmer, Hansen, BIT 2016), an algorithm for non-negative tensor patch dictionary learning in the context of X-ray CT imaging and based on a tensor-tensor p…