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Recycled linear classifiers for multiclass classification

Soni, Akshay; Haupt, Jarvis; Porikli, Fatih

Description

Many machine learning applications employ a multiclass classification stage that uses multiple binary linear classifiers as building blocks. Among these, commonly used strategies such as one-vs-one classification can require learning a large number of hyperplanes, even when the number of classes to be discriminated among is modest. Further, when the data being classified is inherently high-dimensional, the storage and computational complexity associated with the application of multiple linear...[Show more]

dc.contributor.authorSoni, Akshay
dc.contributor.authorHaupt, Jarvis
dc.contributor.authorPorikli, Fatih
dc.coverage.spatialFlorence Italy
dc.date.accessioned2015-12-13T22:31:41Z
dc.date.createdMay 4-9 2014
dc.identifier.isbn9781479928927
dc.identifier.urihttp://hdl.handle.net/1885/75364
dc.description.abstractMany machine learning applications employ a multiclass classification stage that uses multiple binary linear classifiers as building blocks. Among these, commonly used strategies such as one-vs-one classification can require learning a large number of hyperplanes, even when the number of classes to be discriminated among is modest. Further, when the data being classified is inherently high-dimensional, the storage and computational complexity associated with the application of multiple linear classifiers can ignite critical resource management issues. This work describes a novel multiclass classification method based on efficient use of a single 'recycled' linear classifier (or ReLiC), which addresses these storage and implementation complexity issues. The proposed approach amounts to constraining the entire collection of hyperplanes to be circularly-shifted versions of each other, enabling classification procedures that may be implemented with efficient operations, such as circular convolution (which can be efficiently computed using transform domain techniques), and simple sampling/thresholding operations. We show that the optimization task associated with our proposed approach can be formulated as a quadratic program, and we introduce an efficient distributed procedure for its solution based on an alternating direction method of multipliers. Simulation results demonstrate that the performance of the proposed approach is comparable with the more complex, traditional multiclass linear classification strategies, suggesting the proposed approach is a viable alternative in large-scale data classification tasks.
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofseries2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
dc.sourceICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
dc.titleRecycled linear classifiers for multiclass classification
dc.typeConference paper
local.description.notesImported from ARIES
local.description.refereedYes
dc.date.issued2014
local.identifier.absfor080104 - Computer Vision
local.identifier.ariespublicationU3488905xPUB4588
local.type.statusPublished Version
local.contributor.affiliationSoni, Akshay, University of Minnesota
local.contributor.affiliationHaupt, Jarvis, University of Minnesota
local.contributor.affiliationPorikli, Fatih, College of Engineering and Computer Science, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.startpage2957
local.bibliographicCitation.lastpage2961
local.identifier.doi10.1109/ICASSP.2014.6854142
dc.date.updated2015-12-11T09:02:06Z
local.identifier.scopusID2-s2.0-84905270472
CollectionsANU Research Publications

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