Efficient Clustering for Stretched Mixtures: Landscape and Optimality

• URL: http://arxiv.org/abs/2003.09960v3
• Date: Sat, 27 Nov 2021 23:49:35 GMT
• Title: Efficient Clustering for Stretched Mixtures: Landscape and Optimality
• Authors: Kaizheng Wang, Yuling Yan, Mateo D\'iaz
• Abstract summary: This paper considers a canonical clustering problem where one receives unlabeled samples drawn from a balanced mixture of two elliptical distributions. We show that the non-optimal clustering function exhibits desirable geometric properties when the sample size exceeds some constant statistical objectives.
• Score: 4.2111286819721485
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper considers a canonical clustering problem where one receives unlabeled samples drawn from a balanced mixture of two elliptical distributions and aims for a classifier to estimate the labels. Many popular methods including PCA and k-means require individual components of the mixture to be somewhat spherical, and perform poorly when they are stretched. To overcome this issue, we propose a non-convex program seeking for an affine transform to turn the data into a one-dimensional point cloud concentrating around $-1$ and $1$, after which clustering becomes easy. Our theoretical contributions are two-fold: (1) we show that the non-convex loss function exhibits desirable geometric properties when the sample size exceeds some constant multiple of the dimension, and (2) we leverage this to prove that an efficient first-order algorithm achieves near-optimal statistical precision without good initialization. We also propose a general methodology for clustering with flexible choices of feature transforms and loss objectives.

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