Information Retention in Iterative Random Projection of Convex Bodies to Lower Dimensions

In this paper, we consider a bounded convex body $K_0 \subset \mathbb{R}^{n}$ subjected to two successive random orthogonal projections onto $\mathbb{R}^{n-1}$ and $\mathbb{R}^{n-2}$, respectively. First, we project $K_0$ orthogonally onto $U_{1}^{\perp}$, the orthogonal complement of $\mbox{\boldmath $U$}_1$, where $\mbox{\boldmath $U$}_1$ is uniformly distributed on the unit sphere $S^{n-1}$. This yields a random convex body $K_1 = \mathrm{Proj}_{{U_1}^{\perp}}(K_0) \subset \mathbb{R}^{n-1}$. We then repeat the process, projecting $K_1$ orthogonally onto ${U}_{2}^{\perp}$, the orthogonal complement of $\mbox{\boldmath $U$}_2$ chosen uniformly from the unit sphere in $\mbox{\boldmath $U$}_{1}^{\perp}$ or $S^{n-2}$, resulting in a second random convex body $K_2 = \mathrm{Proj}_{{U_2}^{\perp}}(K_1) \subset \mathbb{R}^{n-2}$. To quantify information retention through these sequential dimension reductions, we derive an upper bound for the conditional mutual information $I(K_1;K_2 \mid K_0)$. Furthermore, we extend this process to $m$ iterations and generalize the upper bound on $I(K_1;K_2 \mid K_0)$ to establish an analogous upper bound for $I(K_1;K_m \mid K_0)$. Finally, we examine the influence of $K_0$'s symmetry on the achievability of this upper bound for $I(K_1;K_m \mid K_0)$.

Publication Details

Journal
ArXiv.org
Published
2025-08-13
Primary Topic
Point processes and geometric inequalities
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article
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article

Information Retention in Iterative Random Projection of Convex Bodies to Lower Dimensions

ArXiv.org
Point processes and geometric inequalities
article

Information Retention in Iterative Random Projection of Convex Bodies to Lower Dimensions

article en

Abstract

In this paper, we consider a bounded convex body $K_0 \subset \mathbb{R}^{n}$ subjected to two successive random orthogonal projections onto $\mathbb{R}^{n-1}$ and $\mathbb{R}^{n-2}$, respectively. First, we project $K_0$ orthogonally onto $U_{1}^{\perp}$, the orthogonal complement of $\mbox{\boldmath $U$}_1$, where $\mbox{\boldmath $U$}_1$ is uniformly distributed on the unit sphere $S^{n-1}$. This yields a random convex body $K_1 = \mathrm{Proj}_{{U_1}^{\perp}}(K_0) \subset \mathbb{R}^{n-1}$. We then repeat the process, projecting $K_1$ orthogonally onto ${U}_{2}^{\perp}$, the orthogonal complement of $\mbox{\boldmath $U$}_2$ chosen uniformly from the unit sphere in $\mbox{\boldmath $U$}_{1}^{\perp}$ or $S^{n-2}$, resulting in a second random convex body $K_2 = \mathrm{Proj}_{{U_2}^{\perp}}(K_1) \subset \mathbb{R}^{n-2}$. To quantify information retention through these sequential dimension reductions, we derive an upper bound for the conditional mutual information $I(K_1;K_2 \mid K_0)$. Furthermore, we extend this process to $m$ iterations and generalize the upper bound on $I(K_1;K_2 \mid K_0)$ to establish an analogous upper bound for $I(K_1;K_m \mid K_0)$. Finally, we examine the influence of $K_0$'s symmetry on the achievability of this upper bound for $I(K_1;K_m \mid K_0)$.

ArXiv.org
Openalex Percentile: Top 39%
Point processes and geometric inequalities
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