基地研究员宁瀚文副教授与合作者（Lei Chen, School of Business, Jianghan University; Jiaming Zhang, Department of Statistics, Zhongnan University of Econometrics and Law）的论文“Robust large-scale online kernel learning” 在国际知名期刊Neural Computing and Applications在线发表。

Abstract：

The control-based approach has been proved to be effective for developing robust online learning methods. However, the existing control-based kernel methods are infeasible for large-scale modeling due to their high computational complexity. This paper aims to propose a computationally efficient control-based framework for robust large-scale kernel learning problems. By random feature approximation and robust loss function, the learning problems are first transformed into a group of linear feedback control problems with sparse discrete large-scale algebraic Riccati equations (DARE). Then, with the solutions of the DAREs, two promising algorithms are developed to address large-scale binary classification and regression problems, respectively. Thanks to the sparseness, explicit solutions rather than numerical solutions of the DAREs are derived by utilizing matrix computation techniques developed in our study. This substantially reduces the complexity, and makes the proposed algorithms computationally efficient for large-scale complex datasets. Compared with the existing benchmarks, the proposed algorithms can achieve faster convergent, more robust and accurate modeling results. Theoretical analysis and encouraging numerical results on synthetic and realistic datasets are also provided to illustrate the effectiveness and efficiency of our algorithms.

链接：https://doi.org/10.1007/s00521-022-07283-5

教师简介

宁瀚文，副教授，主持国家自然科学基金两项，国家社会科学基金一项，在科学出版社出版专著一部，论文发表于《中国科学》《统计研究》，IEEE T Neural Networks & Learning Systems、Pattern Recognition、Journal of the Franklin Institute等国内外顶尖期刊。今后的主要研究方向为机器学习，深度学习与经济，金融数据分析的交叉。