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Asynchronous Parallel, Sparse Approximated SVRG for High-Dimensional Machine Learning
- Source :
- IEEE Transactions on Knowledge and Data Engineering. 34:5636-5648
- Publication Year :
- 2022
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2022.
-
Abstract
- With increasing of data size and development of multi-core computers, asynchronous parallel stochastic optimization algorithms such as KroMagnon have gained significant attention. In this paper, we propose a new Sparse approximation and asynchronous parallel Stochastic Variance Reduced Gradient (SSVRG) method for sparse and high-dimensional machine learning problems. Unlike standard SVRG and its asynchronous parallel variant, KroMagnon, the snapshot point of SSVRG is set to the average of all the iterates in the previous epoch, which allows it to take much larger learning rates and makes it more robust to the choice of learning rates. In particular, we use the sparse approximation of the popular SVRG estimator to perform completely sparse updates. Therefore, SSVRG has a much lower per-iteration computational cost than its dense counterpart, SVRG++, and is very friendly to asynchronous parallel implementation. Moreover, we provide the convergence guarantees of SSVRG for both SC and non-SC problems, while existing asynchronous algorithms (e.g., KroMagnon) only have convergence guarantees for SC problems. Finally, we extend SSVRG to non-smooth and asynchronous parallel settings. Numerical results demonstrate that SSVRG converges significantly faster than the state-of-the-art asynchronous parallel methods, e.g., KroMagnon, and is usually more than three orders of magnitude faster than SVRG++.
- Subjects :
- business.industry
Computer science
Estimator
Sparse approximation
Machine learning
computer.software_genre
Computer Science Applications
Set (abstract data type)
Computational Theory and Mathematics
Asynchronous communication
Iterated function
Convergence (routing)
Stochastic optimization
Point (geometry)
Artificial intelligence
business
computer
Information Systems
Subjects
Details
- ISSN :
- 23263865 and 10414347
- Volume :
- 34
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Knowledge and Data Engineering
- Accession number :
- edsair.doi...........1af75126f360d05f7e9d82e4cba60eba