A Bayesian Boolean Matrix Factorization with Application to Copy Number Analysis in Cancer
Binary data factorization is common, but real-valued methods ignore discreteness and yield hard-to-interpret factors. Boolean Matrix Factorization (BooMF) instead decomposes a binary matrix into two lower-rank binary matrices via logical AND and OR, expressing the data as a Boolean disjunction of interpretable patterns. In cancer genomics, BooMF can reveal coordinated feature changes that may drive tumor evolution, unlike rotational or additive decompositions. Most existing BooMF methods are heuristic, greedy, sensitive to initialization, prone to local optima, and do not support principled model selection or uncertainty quantification. We introduce Bayesian Boolean Matrix Factorization (...
| Authors | Adolphus Wagala, Mehmet Samur, Giovanni Parmigiani |
|---|---|
| Primary category | stat.ML |
| Published | 16 June 2026 |
| Last updated at source | 17 June 2026 |
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