Information-theoretic Bounds on Matrix Completion under Union of Subspaces Model

08/14/2015
by   Vaneet Aggarwal, et al.
0

In this short note we extend some of the recent results on matrix completion under the assumption that the columns of the matrix can be grouped (clustered) into subspaces (not necessarily disjoint or independent). This model deviates from the typical assumption prevalent in the literature dealing with compression and recovery for big-data applications. The results have a direct bearing on the problem of subspace clustering under missing or incomplete information.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/23/2011

High-Rank Matrix Completion and Subspace Clustering with Missing Data

This paper considers the problem of completing a matrix with many missin...
research
09/02/2020

Clustering of Nonnegative Data and an Application to Matrix Completion

In this paper, we propose a simple algorithm to cluster nonnegative data...
research
05/10/2016

A note on the statistical view of matrix completion

A very simple interpretation of matrix completion problem is introduced ...
research
04/26/2018

Tensor Methods for Nonlinear Matrix Completion

In the low rank matrix completion (LRMC) problem, the low rank assumptio...
research
05/22/2022

Fusion Subspace Clustering for Incomplete Data

This paper introduces fusion subspace clustering, a novel method to lear...
research
05/07/2018

Matrix Completion with Nonuniform Sampling: Theories and Methods

Prevalent matrix completion theories reply on an assumption that the loc...
research
07/05/2022

Confluent Vandermonde with Arnoldi

In this note, we extend the Vandermonde with Arnoldi method recently adv...

Please sign up or login with your details

Forgot password? Click here to reset