Learning Balanced Mixtures of Discrete Distributions with Small Sample

TL;DR

This paper introduces a graph-based method for partitioning small samples from mixtures of discrete distributions by leveraging high-dimensional features, enabling effective clustering with limited data.

Contribution

It presents a novel approach using maximum-weight balanced cuts on graphs derived from high-dimensional data to accurately partition mixture distributions with small samples.

Findings

High-dimensional features enable clustering with small samples.

The method guarantees correct partitioning under specified conditions.

Trade-offs between feature dimension and sample size are demonstrated.

Abstract

We study the problem of partitioning a small sample of $n$ individuals from a mixture of $k$ product distributions over a Boolean cube $\{0, 1\}^K$ according to their distributions. Each distribution is described by a vector of allele frequencies in $\R^K$. Given two distributions, we use $\gamma$ to denote the average $\ell_2^2$ distance in frequencies across $K$ dimensions, which measures the statistical divergence between them. We study the case assuming that bits are independently distributed across $K$ dimensions. This work demonstrates that, for a balanced input instance for $k = 2$, a certain graph-based optimization function returns the correct partition with high probability, where a weighted graph $G$ is formed over $n$ individuals, whose pairwise hamming distances between their corresponding bit vectors define the edge weights, so long as $K = \Omega(\ln n/\gamma)$ and $Kn = \tilde\Omega(\ln n/\gamma^2)$. The function computes a maximum-weight balanced cut of $G$, where the weight of a cut is the sum of the weights across all edges in the cut. This result demonstrates a nice property in the high-dimensional feature space: one can trade off the number of features that are required with the size of the sample to accomplish certain tasks like clustering.