Column Bound for Orthogonal Matrix Factorization

Fuente: arXiv
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Main Author: Dash, Anirudh
Format: Preprint
Published: 2024
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author Dash, Anirudh
author_facet Dash, Anirudh
contents This article explores the intersection of the Coupon Collector's Problem and the Orthogonal Matrix Factorization (OMF) problem. Specifically, we derive bounds on the minimum number of columns $p$ (in $\mathbf{X}$) required for the OMF problem to be tractable, using insights from the Coupon Collector's Problem. Specifically, we establish a theorem outlining the relationship between the sparsity of the matrix $\mathbf{X}$ and the number of columns $p$ required to recover the matrices $\mathbf{V}$ and $\mathbf{X}$ in the OMF problem. We show that the minimum number of columns $p$ required is given by $p = Ω\left(\max \left\{ \frac{n}{1 - (1 - θ)^n}, \frac{1}θ \log n \right\} \right)$, where $θ$ is the i.i.d Bernoulli parameter from which the sparsity model of the matrix $\mathbf{X}$ is derived.
format Preprint
id arxiv_https___arxiv_org_abs_2405_12858
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Column Bound for Orthogonal Matrix Factorization
Dash, Anirudh
Signal Processing
This article explores the intersection of the Coupon Collector's Problem and the Orthogonal Matrix Factorization (OMF) problem. Specifically, we derive bounds on the minimum number of columns $p$ (in $\mathbf{X}$) required for the OMF problem to be tractable, using insights from the Coupon Collector's Problem. Specifically, we establish a theorem outlining the relationship between the sparsity of the matrix $\mathbf{X}$ and the number of columns $p$ required to recover the matrices $\mathbf{V}$ and $\mathbf{X}$ in the OMF problem. We show that the minimum number of columns $p$ required is given by $p = Ω\left(\max \left\{ \frac{n}{1 - (1 - θ)^n}, \frac{1}θ \log n \right\} \right)$, where $θ$ is the i.i.d Bernoulli parameter from which the sparsity model of the matrix $\mathbf{X}$ is derived.
title Column Bound for Orthogonal Matrix Factorization
topic Signal Processing
url https://arxiv.org/abs/2405.12858