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Conference Paper: Communication efficient parallel algorithms for optimization on manifolds
Title | Communication efficient parallel algorithms for optimization on manifolds |
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Authors | |
Issue Date | 2018 |
Citation | 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada, 2-8 December 2018. In Advances in Neural Information Processing Systems 31 (NeurIPS 2018), 2018, p. 3574-3584 How to Cite? |
Abstract | The last decade has witnessed an explosion in the development of models, theory and computational algorithms for “big data” analysis. In particular, distributed computing has served as a natural and dominating paradigm for statistical inference. However, the existing literature on parallel inference almost exclusively focuses on Euclidean data and parameters. While this assumption is valid for many applications, it is increasingly more common to encounter problems where the data or the parameters lie on a non-Euclidean space, like a manifold for example. Our work aims to fill a critical gap in the literature by generalizing parallel inference algorithms to optimization on manifolds. We show that our proposed algorithm is both communication efficient and carries theoretical convergence guarantees. In addition, we demonstrate the performance of our algorithm to the estimation of Fréchet means on simulated spherical data and the low-rank matrix completion problem over Grassmann manifolds applied to the Netflix prize data set. |
Persistent Identifier | http://hdl.handle.net/10722/296186 |
ISSN | 2020 SCImago Journal Rankings: 1.399 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Saparbayeva, Bayan | - |
dc.contributor.author | Zhang, Michael Minyi | - |
dc.contributor.author | Lin, Lizhen | - |
dc.date.accessioned | 2021-02-11T04:53:01Z | - |
dc.date.available | 2021-02-11T04:53:01Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada, 2-8 December 2018. In Advances in Neural Information Processing Systems 31 (NeurIPS 2018), 2018, p. 3574-3584 | - |
dc.identifier.issn | 1049-5258 | - |
dc.identifier.uri | http://hdl.handle.net/10722/296186 | - |
dc.description.abstract | The last decade has witnessed an explosion in the development of models, theory and computational algorithms for “big data” analysis. In particular, distributed computing has served as a natural and dominating paradigm for statistical inference. However, the existing literature on parallel inference almost exclusively focuses on Euclidean data and parameters. While this assumption is valid for many applications, it is increasingly more common to encounter problems where the data or the parameters lie on a non-Euclidean space, like a manifold for example. Our work aims to fill a critical gap in the literature by generalizing parallel inference algorithms to optimization on manifolds. We show that our proposed algorithm is both communication efficient and carries theoretical convergence guarantees. In addition, we demonstrate the performance of our algorithm to the estimation of Fréchet means on simulated spherical data and the low-rank matrix completion problem over Grassmann manifolds applied to the Netflix prize data set. | - |
dc.language | eng | - |
dc.relation.ispartof | Advances in Neural Information Processing Systems 31 (NeurIPS 2018) | - |
dc.title | Communication efficient parallel algorithms for optimization on manifolds | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.scopus | eid_2-s2.0-85064812619 | - |
dc.identifier.spage | 3574 | - |
dc.identifier.epage | 3584 | - |
dc.identifier.isi | WOS:000461823303056 | - |
dc.identifier.issnl | 1049-5258 | - |