A center sliding Bayesian binary classifier adopting orthogonal polynomials

Lei Sun; Kar Ann Toh; Zhiping Lin

doi:10.1016/j.patcog.2014.12.010

A center sliding Bayesian binary classifier adopting orthogonal polynomials

Lei Sun, Kar Ann Toh, Zhiping Lin

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

9 Citations (Scopus)

Abstract

A center sliding Bayesian design adopting orthogonal polynomials for binary pattern classification is studied in this paper. Essentially, a Bayesian weight solution is coupled with a center sliding scheme in feature space which provides an easy tuning capability for binary classification. The proposed method is compared with several state-of-the-art binary classifiers in terms of their solution forms, decision thresholds and decision boundaries. Based on the center sliding Bayesian framework, a novel orthogonal polynomial classifier is subsequently developed. The orthogonal polynomial classifier is evaluated using two representative orthogonal polynomials for feature mapping. Our experimental results show promising potential of the orthogonal polynomial classifier since it achieves both desired accuracy and computational efficiency.

Original language	English
Pages (from-to)	2013-2028
Number of pages	16
Journal	Pattern Recognition
Volume	48
Issue number	6
DOIs	https://doi.org/10.1016/j.patcog.2014.12.010
Publication status	Published - 2015 Jun 1

Bibliographical note

Funding Information:
The authors would like to thank the anonymous reviewers for their constructive suggestions. Lei Sun is also thankful to Professor Dapeng (Oliver) Wu from University of Florida for hosting his visit during 2011–2012. His kind technical assistance and advice has largely formed the background foundation for this paper. This research was supported by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology ( NRF-2012R1A1A2042428 ).

Publisher Copyright:
© 2014 Elsevier Ltd. All rights reserved.

All Science Journal Classification (ASJC) codes

Software
Signal Processing
Computer Vision and Pattern Recognition
Artificial Intelligence

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10.1016/j.patcog.2014.12.010

Cite this

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title = "A center sliding Bayesian binary classifier adopting orthogonal polynomials",

abstract = "A center sliding Bayesian design adopting orthogonal polynomials for binary pattern classification is studied in this paper. Essentially, a Bayesian weight solution is coupled with a center sliding scheme in feature space which provides an easy tuning capability for binary classification. The proposed method is compared with several state-of-the-art binary classifiers in terms of their solution forms, decision thresholds and decision boundaries. Based on the center sliding Bayesian framework, a novel orthogonal polynomial classifier is subsequently developed. The orthogonal polynomial classifier is evaluated using two representative orthogonal polynomials for feature mapping. Our experimental results show promising potential of the orthogonal polynomial classifier since it achieves both desired accuracy and computational efficiency.",

author = "Lei Sun and Toh, {Kar Ann} and Zhiping Lin",

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PY - 2015/6/1

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N2 - A center sliding Bayesian design adopting orthogonal polynomials for binary pattern classification is studied in this paper. Essentially, a Bayesian weight solution is coupled with a center sliding scheme in feature space which provides an easy tuning capability for binary classification. The proposed method is compared with several state-of-the-art binary classifiers in terms of their solution forms, decision thresholds and decision boundaries. Based on the center sliding Bayesian framework, a novel orthogonal polynomial classifier is subsequently developed. The orthogonal polynomial classifier is evaluated using two representative orthogonal polynomials for feature mapping. Our experimental results show promising potential of the orthogonal polynomial classifier since it achieves both desired accuracy and computational efficiency.

AB - A center sliding Bayesian design adopting orthogonal polynomials for binary pattern classification is studied in this paper. Essentially, a Bayesian weight solution is coupled with a center sliding scheme in feature space which provides an easy tuning capability for binary classification. The proposed method is compared with several state-of-the-art binary classifiers in terms of their solution forms, decision thresholds and decision boundaries. Based on the center sliding Bayesian framework, a novel orthogonal polynomial classifier is subsequently developed. The orthogonal polynomial classifier is evaluated using two representative orthogonal polynomials for feature mapping. Our experimental results show promising potential of the orthogonal polynomial classifier since it achieves both desired accuracy and computational efficiency.

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A center sliding Bayesian binary classifier adopting orthogonal polynomials

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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