Classification of proteomic data with logistic kernel partial least squares algorithm

Zhenqiu Liu; Dechang Chen; Jianjun Tian

doi:10.1109/CVPR.2005.430

Classification of proteomic data with logistic kernel partial least squares algorithm

Zhenqiu Liu, Dechang Chen, Jianjun Tian

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

Abstract

In this paper we introduce the logistic kernel partial least squares (LKPLS) algorithm for classification of health vs. cancer using mass spectrometry (MS). Wavelet decomposition is proposed for feature selection and data preprocessing. LKPLS combines the logistic regression with the kernel partial least squares algorithm. The method is applied to real life cancer samples. Experimental comparisons show that LKPLS outperforms other methods in the analysis of MS data.

Original language	English (US)
Title of host publication	2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - Workshops
Publisher	IEEE Computer Society
ISBN (Electronic)	0769526608
DOIs	https://doi.org/10.1109/CVPR.2005.430
State	Published - 2005
Event	2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - Workshops - San Diego, United States Duration: Sep 21 2005 → Sep 23 2005

Publication series

Name	IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume	2005-September
ISSN (Print)	2160-7508
ISSN (Electronic)	2160-7516

Conference

Conference	2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - Workshops
Country/Territory	United States
City	San Diego
Period	9/21/05 → 9/23/05

All Science Journal Classification (ASJC) codes

Computer Vision and Pattern Recognition
Electrical and Electronic Engineering

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/CVPR.2005.430

Cite this

Liu, Z., Chen, D., & Tian, J. (2005). Classification of proteomic data with logistic kernel partial least squares algorithm. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - Workshops (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2005-September). IEEE Computer Society. https://doi.org/10.1109/CVPR.2005.430

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title = "Classification of proteomic data with logistic kernel partial least squares algorithm",

abstract = "In this paper we introduce the logistic kernel partial least squares (LKPLS) algorithm for classification of health vs. cancer using mass spectrometry (MS). Wavelet decomposition is proposed for feature selection and data preprocessing. LKPLS combines the logistic regression with the kernel partial least squares algorithm. The method is applied to real life cancer samples. Experimental comparisons show that LKPLS outperforms other methods in the analysis of MS data.",

author = "Zhenqiu Liu and Dechang Chen and Jianjun Tian",

note = "Publisher Copyright: {\textcopyright} 2005 IEEE Computer Society. All rights reserved.; 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - Workshops ; Conference date: 21-09-2005 Through 23-09-2005",

year = "2005",

doi = "10.1109/CVPR.2005.430",

language = "English (US)",

series = "IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops",

publisher = "IEEE Computer Society",

booktitle = "2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - Workshops",

address = "United States",

}

Liu, Z, Chen, D & Tian, J 2005, Classification of proteomic data with logistic kernel partial least squares algorithm. in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2005-September, IEEE Computer Society, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - Workshops, San Diego, United States, 9/21/05. https://doi.org/10.1109/CVPR.2005.430

Classification of proteomic data with logistic kernel partial least squares algorithm. / Liu, Zhenqiu; Chen, Dechang; Tian, Jianjun.
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - Workshops. IEEE Computer Society, 2005. (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2005-September).

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

TY - GEN

T1 - Classification of proteomic data with logistic kernel partial least squares algorithm

AU - Liu, Zhenqiu

AU - Chen, Dechang

AU - Tian, Jianjun

PY - 2005

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N2 - In this paper we introduce the logistic kernel partial least squares (LKPLS) algorithm for classification of health vs. cancer using mass spectrometry (MS). Wavelet decomposition is proposed for feature selection and data preprocessing. LKPLS combines the logistic regression with the kernel partial least squares algorithm. The method is applied to real life cancer samples. Experimental comparisons show that LKPLS outperforms other methods in the analysis of MS data.

AB - In this paper we introduce the logistic kernel partial least squares (LKPLS) algorithm for classification of health vs. cancer using mass spectrometry (MS). Wavelet decomposition is proposed for feature selection and data preprocessing. LKPLS combines the logistic regression with the kernel partial least squares algorithm. The method is applied to real life cancer samples. Experimental comparisons show that LKPLS outperforms other methods in the analysis of MS data.

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BT - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - Workshops

PB - IEEE Computer Society

T2 - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005 - Workshops

Y2 - 21 September 2005 through 23 September 2005

ER -

Classification of proteomic data with logistic kernel partial least squares algorithm

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

UN SDGs

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