# Identifiability and Estimation for Unlabeled Finite Mixtures under Marginal Independence

arXiv: https://arxiv.org/abs/2606.07914 · HTML: https://mlsanity.com/paper/2606.07914/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 48 references that are in the map.
- First posted on arXiv 6 Jun 2026.

## Builds on (most cited first, 5 of 5)

- [Large sample analysis of the median heuristic](https://mlsanity.com/paper/1707.07269/) (2017)
- [Invariance assumptions for class distribution estimation](https://mlsanity.com/paper/2311.17225/) (2023)
- [Identifying Finite Mixtures of Nonparametric Product Distributions and Causal Inference of Confounders](https://mlsanity.com/paper/1309.6860/) (2013)
- [DEDPUL: Difference-of-Estimated-Densities-based Positive-Unlabeled Learning](https://mlsanity.com/paper/1902.06965/) (2019)
- [Prior shift estimation for positive unlabeled data through the lens of kernel embedding](https://mlsanity.com/paper/2502.21194/) (2025)

## Abstract (arXiv)

We study component recovery and mixing-matrix estimation from unlabeled finite mixtures whose observable distributions share the same latent components but have unknown mixing weights. The main identifying signal is marginal independence: each component is assumed to be independent on at least one coordinate pair, but no labels, clean component samples, or mixing weights are observed. We first prove a structural result for product components: under a subset-rank condition on the spans of the univariate marginals, any independent affine combination of the components must coincide with a single component. We then extend this principle to observable mixtures and show that, under the corresponding subset-rank, full-rank, and no-cancellation conditions, marginally independent affine combinations recover the corresponding latent components. When every component is independent on some coordinate pair, all components are identifiable, and the mixing matrix is recoverable under the stated completion conditions. Finally, we propose a Product-Marginal Maximum Mean Discrepancy (PM-MMD) estimator over affine combinations of the observable mixtures and prove uniform convergence and stability under approximate marginal independence. This framework also separates the empirical roles of the assumptions: irreducibility is, in general, not directly testable from the unlabeled mixtures alone, whereas marginal independence yields a candidate-level diagnostic through held-out PM-MMD. Controlled and flow-cytometry experiments show when marginal independence provides a useful recovery signal. In the reported multi-component comparisons, condition-aware representative selection stabilizes PM-MMD and improves recovery relative to clustering, factorization, and pairwise mixture-proportion baselines using the same unlabeled mixtures.