Efficient Stochastic Optimization for Low-Rank Distance Metric Learning
Although distance metric learning has been successfully applied to many real-world applications, learning a distance metric from large-scale and high-dimensional data remains a challenging problem. Due to the PSD constraint, the computational complexity of previous algorithms per iteration is at lea...
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ftjaaai:oai:ojs.aaai.org:article/10649 2023-05-15T16:01:35+02:00 Efficient Stochastic Optimization for Low-Rank Distance Metric Learning Zhang, Jie Zhang, Lijun 2017-02-12 application/pdf https://ojs.aaai.org/index.php/AAAI/article/view/10649 https://doi.org/10.1609/aaai.v31i1.10649 eng eng Association for the Advancement of Artificial Intelligence https://ojs.aaai.org/index.php/AAAI/article/view/10649/10508 https://ojs.aaai.org/index.php/AAAI/article/view/10649 doi:10.1609/aaai.v31i1.10649 Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 31 No. 1 (2017): Thirty-First AAAI Conference on Artificial Intelligence 2374-3468 2159-5399 Stochastic Optimization Low-Rank DML Incremental SVD info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2017 ftjaaai https://doi.org/10.1609/aaai.v31i1.10649 2022-07-02T23:42:59Z Although distance metric learning has been successfully applied to many real-world applications, learning a distance metric from large-scale and high-dimensional data remains a challenging problem. Due to the PSD constraint, the computational complexity of previous algorithms per iteration is at least O(d2) where d is the dimensionality of the data.In this paper, we develop an efficient stochastic algorithm for a class of distance metric learning problems with nuclear norm regularization, referred to as low-rank DML. By utilizing the low-rank structure of the intermediate solutions and stochastic gradients, the complexity of our algorithm has a linear dependence on the dimensionality d. The key idea is to maintain all the iterates in factorized representations and construct stochastic gradients that are low-rank. In this way, the projection onto the PSD cone can be implemented efficiently by incremental SVD. Experimental results on several data sets validate the effectiveness and efficiency of our method. Article in Journal/Newspaper DML AAAI Publications (Association for the Advancement of Artificial Intelligence) Proceedings of the AAAI Conference on Artificial Intelligence 31 1 |
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Open Polar |
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AAAI Publications (Association for the Advancement of Artificial Intelligence) |
op_collection_id |
ftjaaai |
language |
English |
topic |
Stochastic Optimization Low-Rank DML Incremental SVD |
spellingShingle |
Stochastic Optimization Low-Rank DML Incremental SVD Zhang, Jie Zhang, Lijun Efficient Stochastic Optimization for Low-Rank Distance Metric Learning |
topic_facet |
Stochastic Optimization Low-Rank DML Incremental SVD |
description |
Although distance metric learning has been successfully applied to many real-world applications, learning a distance metric from large-scale and high-dimensional data remains a challenging problem. Due to the PSD constraint, the computational complexity of previous algorithms per iteration is at least O(d2) where d is the dimensionality of the data.In this paper, we develop an efficient stochastic algorithm for a class of distance metric learning problems with nuclear norm regularization, referred to as low-rank DML. By utilizing the low-rank structure of the intermediate solutions and stochastic gradients, the complexity of our algorithm has a linear dependence on the dimensionality d. The key idea is to maintain all the iterates in factorized representations and construct stochastic gradients that are low-rank. In this way, the projection onto the PSD cone can be implemented efficiently by incremental SVD. Experimental results on several data sets validate the effectiveness and efficiency of our method. |
format |
Article in Journal/Newspaper |
author |
Zhang, Jie Zhang, Lijun |
author_facet |
Zhang, Jie Zhang, Lijun |
author_sort |
Zhang, Jie |
title |
Efficient Stochastic Optimization for Low-Rank Distance Metric Learning |
title_short |
Efficient Stochastic Optimization for Low-Rank Distance Metric Learning |
title_full |
Efficient Stochastic Optimization for Low-Rank Distance Metric Learning |
title_fullStr |
Efficient Stochastic Optimization for Low-Rank Distance Metric Learning |
title_full_unstemmed |
Efficient Stochastic Optimization for Low-Rank Distance Metric Learning |
title_sort |
efficient stochastic optimization for low-rank distance metric learning |
publisher |
Association for the Advancement of Artificial Intelligence |
publishDate |
2017 |
url |
https://ojs.aaai.org/index.php/AAAI/article/view/10649 https://doi.org/10.1609/aaai.v31i1.10649 |
genre |
DML |
genre_facet |
DML |
op_source |
Proceedings of the AAAI Conference on Artificial Intelligence; Vol. 31 No. 1 (2017): Thirty-First AAAI Conference on Artificial Intelligence 2374-3468 2159-5399 |
op_relation |
https://ojs.aaai.org/index.php/AAAI/article/view/10649/10508 https://ojs.aaai.org/index.php/AAAI/article/view/10649 doi:10.1609/aaai.v31i1.10649 |
op_doi |
https://doi.org/10.1609/aaai.v31i1.10649 |
container_title |
Proceedings of the AAAI Conference on Artificial Intelligence |
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31 |
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1 |
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1766397379003547648 |