Zero-Truncated Poisson Tensor Decomposition for Sparse Count Data
- Florida Atlantic Univ., Boca Raton, FL (United States)
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
We propose a novel statistical inference paradigm for zero-inflated multiway count data that dispenses with the need to distinguish between true and false zero counts. Our approach ignores all zero entries and applies zero-truncated Poisson regression on the positive counts. Inference is accomplished via tensor completion that imposes low-rank structure on the Poisson parameter space. Our main result shows that an $$\textit{N}$$-way rank-R parametric tensor š Ļµ (0, ā)$$I$$Ī§āāāĪ§$$I$$ generating Poisson observations can be accurately estimated from approximately $$IR^2 \text{log}^2_2(I)$$ non-zero counts for a nonnegative canonical polyadic decomposition. Several numerical experiments are presented demonstrating that our zero-truncated paradigm is comparable to the ideal scenario where the locations of false zero counts are known $$\textit{a priori}$$.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- DOE Contract Number:
- NA0003525
- OSTI ID:
- 1841834
- Report Number(s):
- SAND2022-0803R; 703028
- Country of Publication:
- United States
- Language:
- English
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