Unsupervised Manifold Linearizing and Clustering

TL;DR

This paper introduces a novel unsupervised method that simultaneously clusters data and learns a linear representation by optimizing the Maximal Coding Rate Reduction metric, effectively handling non-linear manifolds in complex datasets.

Contribution

It proposes a new approach combining representation learning and clustering with a doubly stochastic membership, improving scalability and accuracy over existing methods.

Findings

Outperforms state-of-the-art deep clustering methods on multiple datasets.

Learns a meaningful linear latent representation of complex data.

Demonstrates scalability and effectiveness in real-world datasets.

Abstract

We consider the problem of simultaneously clustering and learning a linear representation of data lying close to a union of low-dimensional manifolds, a fundamental task in machine learning and computer vision. When the manifolds are assumed to be linear subspaces, this reduces to the classical problem of subspace clustering, which has been studied extensively over the past two decades. Unfortunately, many real-world datasets such as natural images can not be well approximated by linear subspaces. On the other hand, numerous works have attempted to learn an appropriate transformation of the data, such that data is mapped from a union of general non-linear manifolds to a union of linear subspaces (with points from the same manifold being mapped to the same subspace). However, many existing works have limitations such as assuming knowledge of the membership of samples to clusters, requiring high sampling density, or being shown theoretically to learn trivial representations. In this paper, we propose to optimize the Maximal Coding Rate Reduction metric with respect to both the data representation and a novel doubly stochastic cluster membership, inspired by state-of-the-art subspace clustering results. We give a parameterization of such a representation and membership, allowing efficient mini-batching and one-shot initialization. Experiments on CIFAR-10, -20, -100, and TinyImageNet-200 datasets show that the proposed method is much more accurate and scalable than state-of-the-art deep clustering methods, and further learns a latent linear representation of the data.