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Strictly positive initialization keeps the Poisson NMF updates well-defined
Statement
Suppose has at least one positive entry in every row and every column. If and , then one complete Poisson NMF multiplicative-update sweep produces strictly positive updated factors and strictly positive rates at both update stages. Consequently every finite number of sweeps from a strictly positive initialization is well-defined.
Statement references
Facts & Assumptions
Given: An admissible count matrix , positive factors , and one ordered sweep of the Poisson NMF algorithm.
Poisson nonnegative matrix-factorization model: every row and column of contains a positive count.
Finite Poisson NMF multiplicative-update sweeps: each update multiplies a positive entry by a row numerator divided by a sum of positive entries; each update multiplies a positive entry by a column numerator divided by a sum of positive updated entries.
Proof
Since are strictly positive, every current rate is positive. For each row , choose with using [F1]. Then for every , the numerator has the positive term , while its denominator is positive. Thus every updated is positive.
The intermediate rates are positive. For each column , choose with using [F1]. For every , the numerator has the positive term at , and its denominator is positive. Thus every updated is positive. Repeating this argument inductively proves the claim for each finite sweep.
Depends on
Used by
Cited to discharge well-definedness by Finite Poisson NMF multiplicative-update sweeps.
Dependency tree · two levels
3 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Daniel D. Lee and H. Sebastian Seung, Algorithms for Non-negative Matrix Factorization, NIPS 2000 (standard reference, not scraped)