Real log canonical threshold of non-negative matrix factorization (Conjecture V.1)
Let have non-negative rank , and suppose that for some strictly positive matrices and . For an NMF model with inner dimension , let denote the real log canonical threshold at for the factorization map , equivalently for the squared-error function with a smooth positive prior. The conjecture is that
References
Primary source
Additional references
- Singularities of Non-negative Matrix Factorization and their application to Bayesian inference — arXiv — Naoki Hayashi, Yota Maeda, Yasushi Esaki
Progress summary
A new unrefereed paper improves the known estimate and settles special cases, but does not settle the full conjecture.
The conjecture asks for exact real log canonical thresholds governing singular behavior in non-negative matrix factorization. Its full scope remains unresolved.
Known results
- Hayashi and Watanabe: an upper bound for the NMF threshold, exact when .
- Related work derives another upper bound for Poisson NMF and likewise identifies exactness when .
- Numerical evidence suggests the threshold can exceed the reduced-rank-regression value, reflecting the distinction between ordinary and non-negative rank.
September 2026 improved bounds
A September 2026 preprint by Naoki Hayashi, Yota Maeda, and Yasushi Esaki gives an improved upper bound for strictly positive factorizations when the true non-negative rank is at least , and proves the exact value when the inner dimension equals both the non-negative rank and ordinary rank. The broader Conjecture V.1 is only partially addressed, and the manuscript is unrefereed.
Current status (as of September 2026): The full conjecture remains open; improved bounds and exact values in the stated special cases are claimed in an unrefereed preprint.
Solutions 0
No solutions have been posted yet.