Logistic Regression Based on Statistical Learning Model with Linearized Kernel for Classification

Authors

  • Xiaochun Guan School of Computer Science and Technology, Zhejiang University of Technology, 310 023 Hangzhou, China
  • Jianhua Zhang School of Computer Science and Technology, Zhejiang University of Technology, 310 023 Hangzhou, China
  • Shengyong Chen School of Computer Science and Technology, Zhejiang University of Technology, 310 023 Hangzhou, China

DOI:

https://doi.org/10.31577/cai_2021_2_298

Keywords:

Elastic net, generalized additive model, kernel, lasso regression, spectra data

Abstract

In this paper, we propose a logistic regression classification method based on the integration of a statistical learning model with linearized kernel pre-processing. The single Gaussian kernel and fusion of Gaussian and cosine kernels are adopted for linearized kernel pre-processing respectively. The adopted statistical learning models are the generalized linear model and the generalized additive model. Using a generalized linear model, the elastic net regularization is adopted to explore the grouping effect of the linearized kernel feature space. Using a generalized additive model, an overlap group-lasso penalty is used to fit the sparse generalized additive functions within the linearized kernel feature space. Experiment results on the Extended Yale-B face database and AR face database demonstrate the effectiveness of the proposed method. The improved solution is also efficiently obtained using our method on the classification of spectra data.

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Published

2021-10-12

How to Cite

Guan, X., Zhang, J., & Chen, S. (2021). Logistic Regression Based on Statistical Learning Model with Linearized Kernel for Classification. Computing and Informatics, 40(2), 298–317. https://doi.org/10.31577/cai_2021_2_298

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