Approximate conditional gradient descent on multi-class classification

Liu, Z; Tsang, I

Approximate conditional gradient descent on multi-class classification

Liu, Z Tsang, I

Permalink

Publication Type:: Conference Proceeding
Citation:: 31st AAAI Conference on Artificial Intelligence, AAAI 2017, 2017, pp. 2301 - 2307
Issue Date:: 2017-01-01

Closed Access

	Filename	Description	Size
	aaai17_grad.pdf	Published version	676.16 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Liu, Z	en_US
dc.contributor.author	Tsang, I https://orcid.org/0000-0001-8095-4637	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	31st AAAI Conference on Artificial Intelligence, AAAI 2017, 2017, pp. 2301 - 2307	en_US
dc.identifier.uri	http://hdl.handle.net/10453/105911
dc.description.abstract	© Copyright 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Conditional gradient descent, aka the Frank-Wolfe algorithm, regains popularity in recent years. The key advantage of Frank-Wolfe is that at each step the expensive projection is replaced with a much more efficient linear optimization step. Similar to gradient descent, the loss function of FrankWolfe scales with the data size. Training on big data poses a challenge for researchers. Recently, stochastic Frank-Wolfe methods have been proposed to solve the problem, but they do not perform well in practice. In this work, we study the problem of approximating the Frank-Wolfe algorithm on the large-scale multi-class classification problem which is a typical application of the Frank-Wolfe algorithm. We present a simple but effective method employing internal structure of data to approximate Frank-Wolfe on the large-scale multiclass classification problem. Empirical results verify that our method outperforms the state-of-the-art stochastic projectionfree methods.	en_US
dc.relation.ispartof	31st AAAI Conference on Artificial Intelligence, AAAI 2017	en_US
dc.title	Approximate conditional gradient descent on multi-class classification	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© Copyright 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Conditional gradient descent, aka the Frank-Wolfe algorithm, regains popularity in recent years. The key advantage of Frank-Wolfe is that at each step the expensive projection is replaced with a much more efficient linear optimization step. Similar to gradient descent, the loss function of FrankWolfe scales with the data size. Training on big data poses a challenge for researchers. Recently, stochastic Frank-Wolfe methods have been proposed to solve the problem, but they do not perform well in practice. In this work, we study the problem of approximating the Frank-Wolfe algorithm on the large-scale multi-class classification problem which is a typical application of the Frank-Wolfe algorithm. We present a simple but effective method employing internal structure of data to approximate Frank-Wolfe on the large-scale multiclass classification problem. Empirical results verify that our method outperforms the state-of-the-art stochastic projectionfree methods.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/105911