SketchyCoreSVD: SketchySVD from Random Subsampling of the Data Matrix

07/31/2019
by   Chandrajit Bajaj, et al.
0

We present a method called SketchyCoreSVD to compute the near-optimal rank r SVD of a data matrix by building random sketches only from its subsampled columns and rows. We provide theoretical guarantees under incoherence assumptions, and validate the performance of our SketchyCoreSVD method on various large static and time-varying datasets.

READ FULL TEXT

page 6

page 7

page 10

page 21

research
12/05/2014

Two step recovery of jointly sparse and low-rank matrices: theoretical guarantees

We introduce a two step algorithm with theoretical guarantees to recover...
research
09/07/2023

Low-rank Matrix Sensing With Dithered One-Bit Quantization

We explore the impact of coarse quantization on low-rank matrix sensing ...
research
06/16/2021

Recovery Guarantees for Time-varying Pairwise Comparison Matrices with Non-transitivity

Pairwise comparison matrices have received substantial attention in a va...
research
09/20/2022

Adapted AZNN Methods for Time-Varying and Static Matrix Problems

We present adapted Zhang Neural Networks (AZNN) in which the parameter s...
research
02/10/2011

Matrix completion with column manipulation: Near-optimal sample-robustness-rank tradeoffs

This paper considers the problem of matrix completion when some number o...
research
08/02/2022

T4DT: Tensorizing Time for Learning Temporal 3D Visual Data

Unlike 2D raster images, there is no single dominant representation for ...
research
02/02/2022

Improving Screening Processes via Calibrated Subset Selection

Many selection processes such as finding patients qualifying for a medic...

Please sign up or login with your details

Forgot password? Click here to reset