ML Anthology

Classification of Proteomic Data with Logistic Kernel Partial Least Squares Algorithm

Zhenqiu Liu, Dechang Chen, Jianjun Paul Tian
CVPR 2005 pp. 145
doi:10.1109/CVPR.2005.430 /cvpr/2005/liu2005cvpr-classification/

Abstract

In this paper we introduce the logistic kernel partial least squares (LKPLS) algorithm for classi?cation 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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Cite

Text

Liu et al. "Classification of Proteomic Data with Logistic Kernel Partial Least Squares Algorithm." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005. doi:10.1109/CVPR.2005.430

Markdown

[Liu et al. "Classification of Proteomic Data with Logistic Kernel Partial Least Squares Algorithm." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2005.](https://mlanthology.org/cvpr/2005/liu2005cvpr-classification/) doi:10.1109/CVPR.2005.430

BibTeX

@inproceedings{liu2005cvpr-classification,
  title     = {{Classification of Proteomic Data with Logistic Kernel Partial Least Squares Algorithm}},
  author    = {Liu, Zhenqiu and Chen, Dechang and Tian, Jianjun Paul},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2005},
  pages     = {145},
  doi       = {10.1109/CVPR.2005.430},
  url       = {https://mlanthology.org/cvpr/2005/liu2005cvpr-classification/}
}