Likelihood Adjusted Semidefinite Programs for Clustering Heterogeneous Data

TL;DR

This paper introduces iLA-SDP, a novel likelihood-adjusted semidefinite programming method for clustering heterogeneous data, which improves stability and accuracy over traditional methods like EM and K-means.

Contribution

The paper extends SDP-based clustering to heterogeneous data by integrating cluster labels as parameters, avoiding centroid estimation, and enhancing stability and exact recovery.

Findings

iLA-SDP achieves lower mis-clustering errors than existing methods.

iLA-SDP is less sensitive to initialization and more stable in high-dimensional settings.

Numeric experiments validate the superior performance of iLA-SDP.

Abstract

Clustering is a widely deployed unsupervised learning tool. Model-based clustering is a flexible framework to tackle data heterogeneity when the clusters have different shapes. Likelihood-based inference for mixture distributions often involves non-convex and high-dimensional objective functions, imposing difficult computational and statistical challenges. The classic expectation-maximization (EM) algorithm is a computationally thrifty iterative method that maximizes a surrogate function minorizing the log-likelihood of observed data in each iteration, which however suffers from bad local maxima even in the special case of the standard Gaussian mixture model with common isotropic covariance matrices. On the other hand, recent studies reveal that the unique global solution of a semidefinite programming (SDP) relaxed $K$-means achieves the information-theoretically sharp threshold for perfectly recovering the cluster labels under the standard Gaussian mixture model. In this paper, we extend the SDP approach to a general setting by integrating cluster labels as model parameters and propose an iterative likelihood adjusted SDP (iLA-SDP) method that directly maximizes the exact observed likelihood in the presence of data heterogeneity. By lifting the cluster assignment to group-specific membership matrices, iLA-SDP avoids centroids estimation -- a key feature that allows exact recovery under well-separateness of centroids without being trapped by their adversarial configurations. Thus iLA-SDP is less sensitive than EM to initialization and more stable on high-dimensional data. Our numeric experiments demonstrate that iLA-SDP can achieve lower mis-clustering errors over several widely used clustering methods including $K$-means, SDP and EM algorithms.