Autoencoded UMAP-Enhanced Clustering for Unsupervised Learning

TL;DR

This paper introduces AUEC, a three-stage unsupervised learning framework combining autoencoders and UMAP to produce a low-dimensional embedding that enhances clustering performance, demonstrated effectively on MNIST data.

Contribution

The paper presents a novel integration of autoencoders and UMAP with spectral graph theory to improve clustering in unsupervised learning.

Findings

AUEC outperforms state-of-the-art clustering methods on MNIST.

The combined embedding enhances clusterability of data.

The framework effectively integrates multiple unsupervised learning components.

Abstract

We propose a novel approach to unsupervised learning by constructing a non-linear embedding of the data into a low-dimensional space followed by any conventional clustering algorithm. The embedding promotes clusterability of the data and is comprised of two mappings: the encoder of an autoencoder neural network and the output of UMAP algorithm. The autoencoder is trained with a composite loss function that incorporates both a conventional data reconstruction as a regularization component and a clustering-promoting component built using the spectral graph theory. The two embeddings and the subsequent clustering are integrated into a three-stage unsupervised learning framework, referred to as Autoencoded UMAP-Enhanced Clustering (AUEC). When applied to MNIST data, AUEC significantly outperforms the state-of-the-art techniques in terms of clustering accuracy.