Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866916911503114240 |
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| author | Hu, Jiuyun Shi, Naichen Kontar, Raed Al Yan, Hao |
| author_facet | Hu, Jiuyun Shi, Naichen Kontar, Raed Al Yan, Hao |
| contents | We propose personalized Tucker decomposition (perTucker) to address the limitations of traditional tensor decomposition methods in capturing heterogeneity across different datasets. perTucker decomposes tensor data into shared global components and personalized local components. We introduce a mode orthogonality assumption and develop a proximal gradient regularized block coordinate descent algorithm that is guaranteed to converge to a stationary point. By learning unique and common representations across datasets, we demonstrate perTucker's effectiveness in anomaly detection, client classification, and clustering through a simulation study and two case studies on solar flare detection and tonnage signal classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_03439 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data Hu, Jiuyun Shi, Naichen Kontar, Raed Al Yan, Hao Machine Learning Methodology We propose personalized Tucker decomposition (perTucker) to address the limitations of traditional tensor decomposition methods in capturing heterogeneity across different datasets. perTucker decomposes tensor data into shared global components and personalized local components. We introduce a mode orthogonality assumption and develop a proximal gradient regularized block coordinate descent algorithm that is guaranteed to converge to a stationary point. By learning unique and common representations across datasets, we demonstrate perTucker's effectiveness in anomaly detection, client classification, and clustering through a simulation study and two case studies on solar flare detection and tonnage signal classification. |
| title | Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2309.03439 |