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Differential Geometry of the Fixed-Rank Core Covariance Manifold

Bongjung Sung

TL;DR

This work develops a rigorous differential-geometry framework for the fixed-rank core covariance manifold arising in matrix-variate data, showing that rank-r cores form a compact smooth embedded submanifold once canonical-decomposability is removed and revealing a diffeomorphic product decomposition of the full PSD cone with Kronecker-separable and core components. It derives explicit Riemannian gradients and Hessians for these manifolds, and formulates a partial-isotropy core shrinkage estimator (PICSE) that leverages the core's low-dimensional structure via second-order Riemannian optimization. Numerical experiments demonstrate that PICSE improves estimation of the core and, consequently, the full covariance in high-dimensional regimes where n<p, particularly when the population core has a low-dimensional feature. The paper thus connects algebraic geometry, quotient geometry, and Riemannian optimization to address high-dimensional covariance estimation beyond separability.

Abstract

We study the differential geometry of the fixed-rank core covariance manifold. According to Hoff, McCormack, and Zhang [J. R. Stat. Soc., B: Stat., 85 (2023), pp. 1659--1679], every covariance matrix $Σ$ of $p_1\times p_2$ matrix-variate data uniquely decomposes into a separable component $K$ and a core component $C$. Such a decomposition also exists for rank-$r$ $Σ$ if $p_1/p_2+p_2/p_1<r$, with $C$ sharing the same rank. They posed an open question on whether a partial-isotropy structure can be imposed on $C$ for high-dimensional covariance estimation. We address this question by showing that a partial-isotropy rank-$r$ core is a non-trivial convex combination of a rank-$r$ core and $I_p$ for $p:=p_1p_2$, motivating the study of rank-$r$ cores. For fixed $r>p_1/p_2+p_2/p_1$, we prove that the set of rank-$r$ cores, $\mathcal{C}_{p_1,p_2,r}^+$, is a compact, smooth, embedded submanifold of the set of rank-$r$ positive semi-definite matrices, except for a measure-zero subset associated with canonical decomposability. When $r=p$, the set of full-rank cores $\mathcal{C}_{p_1,p_2}^{++}$ is itself a smooth manifold. Moreover, the positive definite cone $\mathcal{S}_p^{++}$ is diffeomorphic to the product of the Kronecker and core covariance manifolds, providing new geometric insight into $\mathcal{S}_p^{++}$ via separability. Differential geometric quantities, such as the differential of the diffeomorphism, as well as the Riemannian gradient and Hessian operator on $\mathcal{C}_{p_1,p_2}^{++}$ and the manifolds used in constructing $\mathcal{C}_{p_1,p_2,r}^+$, are also derived. Lastly, we propose a partial-isotropy core shrinkage estimator for matrix-variate data, supported by numerical illustrations.

Differential Geometry of the Fixed-Rank Core Covariance Manifold

TL;DR

This work develops a rigorous differential-geometry framework for the fixed-rank core covariance manifold arising in matrix-variate data, showing that rank-r cores form a compact smooth embedded submanifold once canonical-decomposability is removed and revealing a diffeomorphic product decomposition of the full PSD cone with Kronecker-separable and core components. It derives explicit Riemannian gradients and Hessians for these manifolds, and formulates a partial-isotropy core shrinkage estimator (PICSE) that leverages the core's low-dimensional structure via second-order Riemannian optimization. Numerical experiments demonstrate that PICSE improves estimation of the core and, consequently, the full covariance in high-dimensional regimes where n<p, particularly when the population core has a low-dimensional feature. The paper thus connects algebraic geometry, quotient geometry, and Riemannian optimization to address high-dimensional covariance estimation beyond separability.

Abstract

We study the differential geometry of the fixed-rank core covariance manifold. According to Hoff, McCormack, and Zhang [J. R. Stat. Soc., B: Stat., 85 (2023), pp. 1659--1679], every covariance matrix of matrix-variate data uniquely decomposes into a separable component and a core component . Such a decomposition also exists for rank- if , with sharing the same rank. They posed an open question on whether a partial-isotropy structure can be imposed on for high-dimensional covariance estimation. We address this question by showing that a partial-isotropy rank- core is a non-trivial convex combination of a rank- core and for , motivating the study of rank- cores. For fixed , we prove that the set of rank- cores, , is a compact, smooth, embedded submanifold of the set of rank- positive semi-definite matrices, except for a measure-zero subset associated with canonical decomposability. When , the set of full-rank cores is itself a smooth manifold. Moreover, the positive definite cone is diffeomorphic to the product of the Kronecker and core covariance manifolds, providing new geometric insight into via separability. Differential geometric quantities, such as the differential of the diffeomorphism, as well as the Riemannian gradient and Hessian operator on and the manifolds used in constructing , are also derived. Lastly, we propose a partial-isotropy core shrinkage estimator for matrix-variate data, supported by numerical illustrations.

Paper Structure

This paper contains 30 sections, 25 theorems, 152 equations, 6 figures, 3 tables, 1 algorithm.

Key Result

Proposition 2.2

For $C\in \mathcal{C}_{p_1,p_2}^{++}$, suppose $C=BB^\top+\lambda I_p$ for some $B\in \mathbb{R}_*^{p\times r}$ and constant $\lambda>0$, where $p_1/p_2+p_2/p_1<r$. Then $\lambda\in(0,1)$ and $BB^\top=(1-\lambda)AA^\top$ for a rank$-r$ core $AA^\top$ with $A\in\mathbb{R}_{*}^{p\times r}$.

Figures (6)

  • Figure 1: The boxplot of the relative norm $||\hat{\Sigma}-\Sigma||_2/||\Sigma||_2$ by KMLE, CSE, Base-AI, PI-AI, and PI-Chol, and the sample size $n=p/8,p/4,p/2,p,2p$ across $100$ iterations when $(p_1,p_2,r)=(16,10,4)$. Base-AI and Base-Chol yields the same $\hat{\Sigma}$, and thus the result is reported only for Base-AI as a representative.
  • Figure 1: The boxplots of the relative norm $||\hat{K}-K||_2/||K||_2$ by Base-AI, PI-AI, and PI-Chol, and the sample size $n=p/8,p/4,p/2,p,2p$ across $100$ iterations when $(p_1,p_2,r)=(16,10,4)$. The estimators KMLE, CSE and Base-Chol have the same relative norm as Base-AI, and hence omitted.
  • Figure 2: The boxplot of the relative norm $||\hat{\Sigma}-\Sigma||_2/||\Sigma||_2$ by KMLE, CSE, Base-AI, PI-AI, and PI-Chol, and the sample size $n=p/8,p/4,p/2,p,2p$ across $100$ iterations when $(p_1,p_2,r)=(12,8,4)$. Base-AI and Base-Chol yields the same $\hat{\Sigma}$, and thus the result is reported only for Base-AI as a representative.
  • Figure 2: The boxplot of the relative norm $||\hat{K}-K||_2/||K||_2$ by Base-AI, PI-AI, and PI-Chol, and the sample size $n=p/8,p/4,p/2,p,2p$ across $100$ iterations when $(p_1,p_2,r)=(12,8,4)$. The estimators KMLE, CSE and Base-Chol have the same relative norm as Base-AI, and hence omitted.
  • Figure 3: The boxplots of the relative norm $||\hat{C}-C||_2/||C||_2$ by KMLE, CSE, Base-AI, Base-Chol, PI-AI, and PI-Chol, and the sample size $n=p/8,p/4,p/2,p,2p$ across $100$ iterations when $(p_1,p_2,r)=(16,10,4)$.
  • ...and 1 more figures

Theorems & Definitions (70)

  • Definition 2.1
  • Proposition 2.2
  • Proof 1
  • Example 2.3
  • Proposition 2.4
  • Proof 2
  • Proposition 2.5
  • Proof 3
  • Lemma 2.6
  • Definition 2.7
  • ...and 60 more