Autoencoded UMAP-Enhanced Clustering for Unsupervised Learning

• URL: http://arxiv.org/abs/2501.07729v1
• Date: Mon, 13 Jan 2025 22:30:38 GMT
• Title: Autoencoded UMAP-Enhanced Clustering for Unsupervised Learning
• Authors: Malihehsadat Chavooshi, Alexander V. Mamonov,
• Abstract summary: 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. When applied to MNIST data, AUEC significantly outperforms the state-of-the-art techniques in terms of clustering accuracy.
• Score: 49.1574468325115
• License:
• 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.

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