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

Autor:	Xiaochun GUAN, Jianhua ZHANG, Shengyong CHEN
Předmět:	STATISTICAL learning STATISTICAL models ADDITIVE functions CLASSIFICATION KERNEL operating systems COSINE function LOGISTIC regression analysis
Zdroj:	Computing & Informatics; 2021, Vol. 40 Issue 2, p298-317, 20p
Abstrakt:	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. [ABSTRACT FROM AUTHOR]
Databáze:	Supplemental Index
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