64 citations · 195 across the 23 of their papers we have counts for
30 papers
Boosting Nyström Method
Keaton Hamm, Zhaoying Lu, Wenbo Ouyang +1
The Nyström method is an effective tool to generate low-rank approximations of large matrices, and it is particularly useful for kernel-based learning. To improve the standard Nyst…
Linearized Wasserstein dimensionality reduction with approximation guarantees
Alexander Cloninger, Keaton Hamm, Varun Khurana +1
We introduce LOT Wassmap, a computationally feasible algorithm to uncover low-dimensional structures in the Wasserstein space. The algorithm is motivated by the observation that ma…
Multi-Priority Graph Sparsification
Reyan Ahmed, Keaton Hamm, Stephen Kobourov +3
A \emph{sparsification} of a given graph is a sparser graph (typically a subgraph) which aims to approximate or preserve some property of . Examples of sparsifications inclu…
Generalized Pseudoskeleton Decompositions
Keaton Hamm
We characterize some variations of pseudoskeleton (also called CUR) decompositions for matrices and tensors over arbitrary fields. These characterizations extend previous results t…
Riemannian CUR Decompositions for Robust Principal Component Analysis
Keaton Hamm, Mohamed Meskini, HanQin Cai
Robust Principal Component Analysis (PCA) has received massive attention in recent years. It aims to recover a low-rank matrix and a sparse matrix from their sum. This paper propos…
Wassmap: Wasserstein Isometric Mapping for Image Manifold Learning
Keaton Hamm, Nick Henscheid, Shujie Kang
In this paper, we propose Wasserstein Isometric Mapping (Wassmap), a nonlinear dimensionality reduction technique that provides solutions to some drawbacks in existing global nonli…