Heteroskedastic Canonical Polyadic Tensor Decomposition

Kyle Ritscher; Carlos Llosa-Vite

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arXiv · 2610.00498

Heteroskedastic Canonical Polyadic Tensor Decomposition

Kyle Ritscher·Carlos Llosa-Vite

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Abstract

When minimizing the squared-error loss, the popular CP decomposition can be interpreted as parameter inference in a Gaussian model with a low-rank mean tensor and constant variance across the tensor entries. We introduce heteroskedastic-CP (HCP), which models entrywise variability with a non-constant, low-rank precision tensor, and develop an alternating block-coordinate ascent method to recover both the low-rank mean and precision tensors from noisy observations. Our procedure is computationally competitive, with the same leading-order factor-update complexity as CP-ALS. We demonstrate HCP on synthetic experiments and an EEG application.

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Kyle Ritscher, Carlos Llosa-Vite. 2026-09-30. Heteroskedastic Canonical Polyadic Tensor Decomposition. https://arxiv.org/abs/2610.00498

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