Tensor Covariance Estimation via Kronecker-Structured Sparse Inverse Cholesky
基于Kronecker结构稀疏逆Cholesky的张量协方差估计
High-dimensional multi-way (tensor) data pose significant challenges for covariance estimation due to the curse of dimensionality. We introduce a unified framework for scalable estimation of tensor covariances based on a Kronecker-structured sparse inverse Cholesky (KSIC) projection. Our approach is grounded in the geometry of information projection, defining the estimator as the moment-matching projection of a target distribution onto a manifold characterized by sparse,…