TY - JOUR AU - Guan, Xiaochun AU - Zhang, Jianhua AU - Chen, Shengyong PY - 2021/10/12 Y2 - 2024/03/28 TI - Logistic Regression Based on Statistical Learning Model with Linearized Kernel for Classification JF - COMPUTING AND INFORMATICS JA - Comput. Inform. VL - 40 IS - 2 SE - Articles DO - 10.31577/cai_2021_2_298 UR - https://www.cai.sk/ojs/index.php/cai/article/view/2021_2_298 SP - 298–317 AB - <p>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.</p> ER -