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An efficient EM algorithm for both element-wise and structural missingness in matrix-variate normal mixture models

arXiv · AI, language, vision and robotics · article · Sep 1, 2026 · UTC

Matrix-variate data with missing entries arise frequently in applications where observations are naturally organized as two-dimensional arrays. Although the matrix normal distribution provides a parsimonious model through its Kronecker covariance structure, standard EM estimation can be computationally expensive because arbitrary missingness patterns typically destroy this separability in the E-step. In this paper, we propose an efficient partial EM algorithm for matrix-variate normal data with missing entries. The proposed method updates the conditional mean and covariance of the missing comp

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