Associate Professor Hanwen Ning, a researcher of the Base, has published a collaborative paper titled "Robust large-scale online kernel learning" in Neural Computing and Applications

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.

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

Teacher Profile

Hanwen Ning , Associate professor, presided over two projects funded by the National Natural Science Foundation and one funded by the National Social Science Foundation. He published a monograph in Science Press, and his papers were published in Science China Press and Statistical Research. IEEE T Neural Networks & Learning Systems, Pattern Recognition, Journal of the Franklin Institute and other top journals at home and abroad. In the future, my main research direction is the intersection of machine learning, deep learning and economic and financial data analysis.