Scalable Multi-view Clustering via Explicit Kernel Features Maps
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911390519787520 |
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| author | Fettal, Chakib Labiod, Lazhar Nadif, Mohamed |
| author_facet | Fettal, Chakib Labiod, Lazhar Nadif, Mohamed |
| contents | The proliferation of high-dimensional data from sources such as social media, sensor networks, and online platforms has created new challenges for clustering algorithms. Multi-view clustering, which integrates complementary information from multiple data perspectives, has emerged as a powerful solution. However, existing methods often struggle with scalability and efficiency, particularly on large attributed networks. In this work, we address these limitations by leveraging explicit kernel feature maps and a non-iterative optimization strategy, enabling efficient and accurate clustering on datasets with millions of points. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_04794 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scalable Multi-view Clustering via Explicit Kernel Features Maps Fettal, Chakib Labiod, Lazhar Nadif, Mohamed Machine Learning The proliferation of high-dimensional data from sources such as social media, sensor networks, and online platforms has created new challenges for clustering algorithms. Multi-view clustering, which integrates complementary information from multiple data perspectives, has emerged as a powerful solution. However, existing methods often struggle with scalability and efficiency, particularly on large attributed networks. In this work, we address these limitations by leveraging explicit kernel feature maps and a non-iterative optimization strategy, enabling efficient and accurate clustering on datasets with millions of points. |
| title | Scalable Multi-view Clustering via Explicit Kernel Features Maps |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.04794 |