Memory-efficient large-scale linear support vector machine
Memory-efficient large-scale linear support vector machine
Stochastic gradient descent has been advanced as a computationally efficient method for large-scale problems. In classification problems, many proposed linear support vector machines as very effective classifiers. However, they assume that the data is already in memory which might not be always the case. Recent work suggests a classical method that divides such a problem into smaller blocks and then solves the sub-problems iteratively. We show that a simple modification of shrinking the dataset early will produce significant saving in computation and memory. We further find that on problems larger than previously considered, our approach is able to reach solutions on top-end desktop machines while competing methods cannot.
Alrajeh, Abdullah
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Takeda, Akiko
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Niranjan, Mahesan
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November 2014
Alrajeh, Abdullah
64acb5ae-6e8e-44a0-9afa-edd1658cd0cd
Takeda, Akiko
c290c3e2-3d52-445b-99a8-0bbfbade270b
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Alrajeh, Abdullah, Takeda, Akiko and Niranjan, Mahesan
(2014)
Memory-efficient large-scale linear support vector machine.
The 7th International Conference on Machine Vision, Milano, Italy.
19 - 21 Nov 2014.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Stochastic gradient descent has been advanced as a computationally efficient method for large-scale problems. In classification problems, many proposed linear support vector machines as very effective classifiers. However, they assume that the data is already in memory which might not be always the case. Recent work suggests a classical method that divides such a problem into smaller blocks and then solves the sub-problems iteratively. We show that a simple modification of shrinking the dataset early will produce significant saving in computation and memory. We further find that on problems larger than previously considered, our approach is able to reach solutions on top-end desktop machines while competing methods cannot.
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icmv14.pdf
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Published date: November 2014
Venue - Dates:
The 7th International Conference on Machine Vision, Milano, Italy, 2014-11-19 - 2014-11-21
Organisations:
Electronics & Computer Science
Identifiers
Local EPrints ID: 368208
URI: http://eprints.soton.ac.uk/id/eprint/368208
PURE UUID: 0bd228fe-5db4-43fe-a99e-534039190ef2
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Date deposited: 20 Aug 2014 15:26
Last modified: 15 Mar 2024 03:29
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Contributors
Author:
Abdullah Alrajeh
Author:
Akiko Takeda
Author:
Mahesan Niranjan
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