148 citations · 790 across the 56 of their papers we have counts for
3 papers · 1 filter
Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data
Joel A. Tropp, Alp Yurtsever, Madeleine Udell +1
Several important applications, such as streaming PCA and semidefinite programming, involve a large-scale positive-semidefinite (psd) matrix that is presented as a sequence of line…
Why are Big Data Matrices Approximately Low Rank?
Madeleine Udell, Alex Townsend
Matrices of (approximate) low rank are pervasive in data science, appearing in recommender systems, movie preferences, topic models, medical records, and genomics. While there is a…
Sketchy Decisions: Convex Low-Rank Matrix Optimization with Optimal Storage
Alp Yurtsever, Madeleine Udell, Joel A. Tropp +1
This paper concerns a fundamental class of convex matrix optimization problems. It presents the first algorithm that uses optimal storage and provably computes a low-rank approxima…