High-dimensional multi-view clustering methods
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866914167503454208 |
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| author | Zahir, Alaeddine Jbilou, Khalide Ratnani, Ahmed |
| author_facet | Zahir, Alaeddine Jbilou, Khalide Ratnani, Ahmed |
| contents | Multi-view clustering has been widely used in recent years in comparison to single-view clustering, for clear reasons, as it offers more insights into the data, which has brought with it some challenges, such as how to combine these views or features. Most of recent work in this field focuses mainly on tensor representation instead of treating the data as simple matrices. This permits to deal with the high-order correlation between the data which the based matrix approach struggles to capture. Accordingly, we will examine and compare these approaches, particularly in two categories, namely graph-based clustering and subspace-based clustering. We will conduct and report experiments of the main clustering methods over a benchmark datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_08582 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | High-dimensional multi-view clustering methods Zahir, Alaeddine Jbilou, Khalide Ratnani, Ahmed Machine Learning Numerical Analysis Multi-view clustering has been widely used in recent years in comparison to single-view clustering, for clear reasons, as it offers more insights into the data, which has brought with it some challenges, such as how to combine these views or features. Most of recent work in this field focuses mainly on tensor representation instead of treating the data as simple matrices. This permits to deal with the high-order correlation between the data which the based matrix approach struggles to capture. Accordingly, we will examine and compare these approaches, particularly in two categories, namely graph-based clustering and subspace-based clustering. We will conduct and report experiments of the main clustering methods over a benchmark datasets. |
| title | High-dimensional multi-view clustering methods |
| topic | Machine Learning Numerical Analysis |
| url | https://arxiv.org/abs/2303.08582 |