12 citations · 27 across the 15 of their papers we have counts for
18 papers
Stochastic Iterative Methods for Online Rank Aggregation from Pairwise Comparisons
Benjamin Jarman, Lara Kassab, Deanna Needell +1
In this paper, we consider large-scale ranking problems where one is given a set of (possibly non-redundant) pairwise comparisons and the underlying ranking explained by those comp…
Block Matrix and Tensor Randomized Kaczmarz Methods for Linear Feasibility Problems
Minxin Zhang, Jamie Haddock, Deanna Needell
The randomized Kaczmarz methods are a popular and effective family of iterative methods for solving large-scale linear systems of equations, which have also been applied to linear…
Kernel Alignment for Unsupervised Feature Selection via Matrix Factorization
Ziyuan Lin, Deanna Needell
By removing irrelevant and redundant features, feature selection aims to find a good representation of the original features. With the prevalence of unlabeled data, unsupervised fe…
Stratified-NMF for Heterogeneous Data
James Chapman, Yotam Yaniv, Deanna Needell
Non-negative matrix factorization (NMF) is an important technique for obtaining low dimensional representations of datasets. However, classical NMF does not take into account data…
Fast and Low-Memory Compressive Sensing Algorithms for Low Tucker-Rank Tensor Approximation from Streamed Measurements
Cullen Haselby, Mark A. Iwen, Deanna Needell +2
In this paper we consider the problem of recovering a low-rank Tucker approximation to a massive tensor based solely on structured random compressive measurements. Crucially, the p…
Stochastic Natural Thresholding Algorithms
Rachel Grotheer, Shuang Li, Anna Ma +2
Sparse signal recovery is one of the most fundamental problems in various applications, including medical imaging and remote sensing. Many greedy algorithms based on the family of…