LOGISTIC REGRESSION BASED ON STATISTICAL LEARNING MODEL WITH LINEARIZED KERNEL FOR CLASSIFICATION

Guan Xiaochun; Zhang Jianhua; Chen Shengyong

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LOGISTIC REGRESSION BASED ON STATISTICAL LEARNING MODEL WITH LINEARIZED KERNEL FOR CLASSIFICATION

机译：LOGISTIC REGRESSION BASED ON STATISTICAL LEARNING MODEL WITH LINEARIZED KERNEL FOR CLASSIFICATION

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摘要

In this paper, we propose a logistic regression classification method based on the integration of a statistical learning model with linearized kernel preprocessing. 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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来源
《Computing and informatics》 |2021年第2期|298-317|共20页
作者
Guan Xiaochun; Zhang Jianhua; Chen Shengyong;
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Zhejiang Univ Technol, Sch Comp Sci & Technol, Hangzhou 310023, Peoples R China;

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正文语种英语
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关键词
Elastic net; generalized additive model; kernel; lasso regression; spectra data;

LOGISTIC REGRESSION BASED ON STATISTICAL LEARNING MODEL WITH LINEARIZED KERNEL FOR CLASSIFICATION

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