Autoencoded UMAP-Enhanced Clustering for Unsupervised Learning
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915207323844608 |
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| author | Chavooshi, Malihehsadat Mamonov, Alexander V. |
| author_facet | Chavooshi, Malihehsadat Mamonov, Alexander V. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_07729 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Autoencoded UMAP-Enhanced Clustering for Unsupervised Learning Chavooshi, Malihehsadat Mamonov, Alexander V. Machine Learning 68Q32, 68T07, 68T09, 68T10 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. |
| title | Autoencoded UMAP-Enhanced Clustering for Unsupervised Learning |
| topic | Machine Learning 68Q32, 68T07, 68T09, 68T10 |
| url | https://arxiv.org/abs/2501.07729 |