Online Functional Principal Component Analysis on a Multidimensional Domain
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912360755625984 |
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| author | Nanshan, Muye Zhang, Nan Cao, Jiguo |
| author_facet | Nanshan, Muye Zhang, Nan Cao, Jiguo |
| contents | Multidimensional functional data streams arise in diverse scientific fields, yet their analysis poses significant challenges. We propose a novel online framework for functional principal component analysis that enables efficient and scalable modeling of such data. Our method represents functional principal components using tensor product splines, enforcing smoothness and orthonormality through a penalized framework on a Stiefel manifold. An efficient Riemannian stochastic gradient descent algorithm is developed, with extensions inspired by adaptive moment estimation and averaging techniques to accelerate convergence. Additionally, a dynamic tuning strategy for smoothing parameter selection is developed based on a rolling averaged block validation score that adapts to the streaming nature of the data. Extensive simulations and real-world applications demonstrate the flexibility and effectiveness of this framework for analyzing multidimensional functional data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_02131 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Online Functional Principal Component Analysis on a Multidimensional Domain Nanshan, Muye Zhang, Nan Cao, Jiguo Methodology Machine Learning Multidimensional functional data streams arise in diverse scientific fields, yet their analysis poses significant challenges. We propose a novel online framework for functional principal component analysis that enables efficient and scalable modeling of such data. Our method represents functional principal components using tensor product splines, enforcing smoothness and orthonormality through a penalized framework on a Stiefel manifold. An efficient Riemannian stochastic gradient descent algorithm is developed, with extensions inspired by adaptive moment estimation and averaging techniques to accelerate convergence. Additionally, a dynamic tuning strategy for smoothing parameter selection is developed based on a rolling averaged block validation score that adapts to the streaming nature of the data. Extensive simulations and real-world applications demonstrate the flexibility and effectiveness of this framework for analyzing multidimensional functional data. |
| title | Online Functional Principal Component Analysis on a Multidimensional Domain |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2505.02131 |