文章基本信息

标题：Logistic Regression Based on Statistical Learning Model with Linearized Kernel for Classification
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作者：Xiaochun Guan ; Jianhua Zhang ; Shengyong Chen 等
期刊名称：COMPUTING AND INFORMATICS
印刷版ISSN：1335-9150
出版年度：2021
卷号：40
期号：2
页码：298-317
DOI：10.31577/cai_2021_2_298
语种：English
出版社：COMPUTING AND INFORMATICS
摘要：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.
关键词：Elastic net;generalized additive model;kernel;lasso regression;spectra data