Algorithms for ridge estimation with convergence guarantees
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2021
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| Accesso online: | |
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| _version_ | 1866913629966696448 |
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| author | Qiao, Wanli Polonik, Wolfgang |
| author_facet | Qiao, Wanli Polonik, Wolfgang |
| contents | The extraction of filamentary structure from a point cloud is discussed. The filaments are modeled as ridge lines or higher dimensional ridges of an underlying density. We propose two novel algorithms, and provide theoretical guarantees for their convergences, by which we mean that the algorithms can asymptotically recover the full ridge set. We consider the new algorithms as alternatives to the Subspace Constrained Mean Shift (SCMS) algorithm for which no such theoretical guarantees are known. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2104_12314 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Algorithms for ridge estimation with convergence guarantees Qiao, Wanli Polonik, Wolfgang Machine Learning Statistics Theory 62G05 The extraction of filamentary structure from a point cloud is discussed. The filaments are modeled as ridge lines or higher dimensional ridges of an underlying density. We propose two novel algorithms, and provide theoretical guarantees for their convergences, by which we mean that the algorithms can asymptotically recover the full ridge set. We consider the new algorithms as alternatives to the Subspace Constrained Mean Shift (SCMS) algorithm for which no such theoretical guarantees are known. |
| title | Algorithms for ridge estimation with convergence guarantees |
| topic | Machine Learning Statistics Theory 62G05 |
| url | https://arxiv.org/abs/2104.12314 |