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.
@article{arxiv.2501.07729,
title = {Autoencoded UMAP-Enhanced Clustering for Unsupervised Learning},
author = {Malihehsadat Chavooshi and Alexander V. Mamonov},
journal= {arXiv preprint arXiv:2501.07729},
year = {2025}
}