On semi-supervised classification

Balaji Krishnapuram; David Williams; Ya Xue; Alex Hartemink; Lawrence Carin; Ḿario A.T. Figueiredo

On semi-supervised classification

Balaji Krishnapuram, David Williams, Ya Xue, Alex Hartemink, Lawrence Carin, Ḿario A.T. Figueiredo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A graph-based prior is proposed for parametric semi-supervised classification. The prior utilizes both labelled and unlabelled data; it also integrates features from multiple views of a given sample (e.g., multiple sensors), thus implementing a Bayesian form of co-training. An EM algorithm for training the classifier automatically adjusts the tradeoff between the contributions of: (a) the labelled data; (b) the unlabelled data; and (c) the co-training information. Active label query selection is performed using a mutual information based criterion that explicitly uses the unlabelled data and the co-training information. Encouraging results are presented on public benchmarks and on measured data from single and multiple sensors.

Original language	English (US)
Title of host publication	Advances in Neural Information Processing Systems
Publisher	Neural information processing systems foundation
ISBN (Print)	0262195348
State	Published - Jan 1 2005
Externally published	Yes

Cite this

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title = "On semi-supervised classification",

abstract = "A graph-based prior is proposed for parametric semi-supervised classification. The prior utilizes both labelled and unlabelled data; it also integrates features from multiple views of a given sample (e.g., multiple sensors), thus implementing a Bayesian form of co-training. An EM algorithm for training the classifier automatically adjusts the tradeoff between the contributions of: (a) the labelled data; (b) the unlabelled data; and (c) the co-training information. Active label query selection is performed using a mutual information based criterion that explicitly uses the unlabelled data and the co-training information. Encouraging results are presented on public benchmarks and on measured data from single and multiple sensors.",

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AU - Williams, David

AU - Xue, Ya

AU - Hartemink, Alex

AU - Carin, Lawrence

AU - Figueiredo, Ḿario A.T.

N1 - Generated from Scopus record by KAUST IRTS on 2021-02-09

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N2 - A graph-based prior is proposed for parametric semi-supervised classification. The prior utilizes both labelled and unlabelled data; it also integrates features from multiple views of a given sample (e.g., multiple sensors), thus implementing a Bayesian form of co-training. An EM algorithm for training the classifier automatically adjusts the tradeoff between the contributions of: (a) the labelled data; (b) the unlabelled data; and (c) the co-training information. Active label query selection is performed using a mutual information based criterion that explicitly uses the unlabelled data and the co-training information. Encouraging results are presented on public benchmarks and on measured data from single and multiple sensors.

AB - A graph-based prior is proposed for parametric semi-supervised classification. The prior utilizes both labelled and unlabelled data; it also integrates features from multiple views of a given sample (e.g., multiple sensors), thus implementing a Bayesian form of co-training. An EM algorithm for training the classifier automatically adjusts the tradeoff between the contributions of: (a) the labelled data; (b) the unlabelled data; and (c) the co-training information. Active label query selection is performed using a mutual information based criterion that explicitly uses the unlabelled data and the co-training information. Encouraging results are presented on public benchmarks and on measured data from single and multiple sensors.

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M3 - Conference contribution

SN - 0262195348

BT - Advances in Neural Information Processing Systems

PB - Neural information processing systems foundation

ER -

On semi-supervised classification

Abstract

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