37 citations · 74 across the 6 of their papers we have counts for
5 papers · 1 filter
ALPCAH: Sample-wise Heteroscedastic PCA with Tail Singular Value Regularization
Javier Salazar Cavazos, Jeffrey A. Fessler, Laura Balzano
Principal component analysis (PCA) is a key tool in the field of data dimensionality reduction that is useful for various data science problems. However, many applications involve…
Preference Modeling with Context-Dependent Salient Features
Amanda Bower, Laura Balzano
We consider the problem of estimating a ranking on a set of items from noisy pairwise comparisons given item features. We address the fact that pairwise comparison data often refle…
Streaming PCA and Subspace Tracking: The Missing Data Case
Laura Balzano, Yuejie Chi, Yue M. Lu
For many modern applications in science and engineering, data are collected in a streaming fashion carrying time-varying information, and practitioners need to process them with a…
Tensor Methods for Nonlinear Matrix Completion
Greg Ongie, Daniel Pimentel-Alarcón, Laura Balzano +2
In the low-rank matrix completion (LRMC) problem, the low-rank assumption means that the columns (or rows) of the matrix to be completed are points on a low-dimensional linear alge…
Algebraic Variety Models for High-Rank Matrix Completion
Greg Ongie, Rebecca Willett, Robert D. Nowak +1
We consider a generalization of low-rank matrix completion to the case where the data belongs to an algebraic variety, i.e. each data point is a solution to a system of polynomial…