Efficient approximation of labeling problems with applications to immune repertoire analysis

Yusuf Osmanlioglu; Santiago Ontanon; Uri Hershberg; Ali Shokoufandeh

doi:10.1109/ICPR.2016.7899997

Efficient approximation of labeling problems with applications to immune repertoire analysis

Yusuf Osmanlioglu, Santiago Ontanon, Uri Hershberg, Ali Shokoufandeh

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

Abstract

Labeling problems are finding increasing applications to optimization problems. They usually get realized into linear or quadratic optimization problems, which are inefficient for large graphs. In this paper we propose an efficient primal-dual solution, ML_PD, for a family of labeling problems. We apply this algorithm to the analysis of immune repertoires, and compare it against our baseline approach based on refinement operators. We provide a comparative evaluation both in terms of accuracy and computational efficiency with respect to the baseline model, as well as to quadratic optimization.

Original language	English
Title of host publication	2016 23rd International Conference on Pattern Recognition, ICPR 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2410-2415
Number of pages	6
ISBN (Electronic)	9781509048472
DOIs	https://doi.org/10.1109/ICPR.2016.7899997
State	Published - 1 Jan 2016
Externally published	Yes
Event	23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico Duration: 4 Dec 2016 → 8 Dec 2016

Publication series

Name	Proceedings - International Conference on Pattern Recognition
Volume	0
ISSN (Print)	1051-4651

Conference

Conference	23rd International Conference on Pattern Recognition, ICPR 2016
Country/Territory	Mexico
City	Cancun
Period	4/12/16 → 8/12/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

ASJC Scopus subject areas

Computer Vision and Pattern Recognition

Access to Document

10.1109/ICPR.2016.7899997

Cite this

Osmanlioglu, Y., Ontanon, S., Hershberg, U., & Shokoufandeh, A. (2016). Efficient approximation of labeling problems with applications to immune repertoire analysis. In 2016 23rd International Conference on Pattern Recognition, ICPR 2016 (pp. 2410-2415). Article 7899997 (Proceedings - International Conference on Pattern Recognition; Vol. 0). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPR.2016.7899997

Osmanlioglu, Yusuf ; Ontanon, Santiago ; Hershberg, Uri et al. / Efficient approximation of labeling problems with applications to immune repertoire analysis. 2016 23rd International Conference on Pattern Recognition, ICPR 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 2410-2415 (Proceedings - International Conference on Pattern Recognition).

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title = "Efficient approximation of labeling problems with applications to immune repertoire analysis",

abstract = "Labeling problems are finding increasing applications to optimization problems. They usually get realized into linear or quadratic optimization problems, which are inefficient for large graphs. In this paper we propose an efficient primal-dual solution, MLPD, for a family of labeling problems. We apply this algorithm to the analysis of immune repertoires, and compare it against our baseline approach based on refinement operators. We provide a comparative evaluation both in terms of accuracy and computational efficiency with respect to the baseline model, as well as to quadratic optimization.",

author = "Yusuf Osmanlioglu and Santiago Ontanon and Uri Hershberg and Ali Shokoufandeh",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 23rd International Conference on Pattern Recognition, ICPR 2016 ; Conference date: 04-12-2016 Through 08-12-2016",

year = "2016",

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doi = "10.1109/ICPR.2016.7899997",

language = "English",

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Osmanlioglu, Y, Ontanon, S, Hershberg, U & Shokoufandeh, A 2016, Efficient approximation of labeling problems with applications to immune repertoire analysis. in 2016 23rd International Conference on Pattern Recognition, ICPR 2016., 7899997, Proceedings - International Conference on Pattern Recognition, vol. 0, Institute of Electrical and Electronics Engineers Inc., pp. 2410-2415, 23rd International Conference on Pattern Recognition, ICPR 2016, Cancun, Mexico, 4/12/16. https://doi.org/10.1109/ICPR.2016.7899997

Efficient approximation of labeling problems with applications to immune repertoire analysis. / Osmanlioglu, Yusuf; Ontanon, Santiago; Hershberg, Uri et al.
2016 23rd International Conference on Pattern Recognition, ICPR 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 2410-2415 7899997 (Proceedings - International Conference on Pattern Recognition; Vol. 0).

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

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AB - Labeling problems are finding increasing applications to optimization problems. They usually get realized into linear or quadratic optimization problems, which are inefficient for large graphs. In this paper we propose an efficient primal-dual solution, MLPD, for a family of labeling problems. We apply this algorithm to the analysis of immune repertoires, and compare it against our baseline approach based on refinement operators. We provide a comparative evaluation both in terms of accuracy and computational efficiency with respect to the baseline model, as well as to quadratic optimization.

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M3 - Conference contribution

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Osmanlioglu Y, Ontanon S, Hershberg U, Shokoufandeh A. Efficient approximation of labeling problems with applications to immune repertoire analysis. In 2016 23rd International Conference on Pattern Recognition, ICPR 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 2410-2415. 7899997. (Proceedings - International Conference on Pattern Recognition). doi: 10.1109/ICPR.2016.7899997

Efficient approximation of labeling problems with applications to immune repertoire analysis

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