Conference proceeding
Efficient Approximation of Labeling Problems with Applications to Immune Repertoire Analysis
2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), pp.2410-2415
International Conference on Pattern Recognition
01 Jan 2016
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.
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Details
- Title
- Efficient Approximation of Labeling Problems with Applications to Immune Repertoire Analysis
- Creators
- Yusuf Osmanhoglu - Drexel Univ, Dept Comp Sci, Philadelphia, PA 19104 USASantiago Ontanon - Drexel Univ, Dept Comp Sci, Philadelphia, PA 19104 USAUri Hershberg - Drexel Univ, Dept Biomed Engn Sci & Hlth Syst, Philadelphia, PA 19104 USAAli Shokoufandeh - Drexel Univ, Dept Comp Sci, Philadelphia, PA 19104 USAIEEE
- Publication Details
- 2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR), pp.2410-2415
- Series
- International Conference on Pattern Recognition
- Publisher
- IEEE
- Number of pages
- 6
- Grant note
- IIS-1551338 / National Science Foundation; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science (Computing); School of Biomedical Engineering, Science, and Health Systems
- Identifiers
- 991019170154804721
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