Heteroskedastic Canonical Polyadic Tensor Decomposition
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.
| Authors | Kyle Ritscher, Carlos Llosa-Vite |
|---|---|
| Primary category | stat.ME |
| Published | 30 September 2026 |
| Last updated at source | 9 October 2026 |
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