An Incremental Learning Method for L1- Regularized Kernel Machine in WSN

Abstract

Due to the limited energy, memory space and processing ability on wireless sensor nodes, the batch learning method will be infeasible for larger number of samples or sequence samples. This paper focuses on the incremental learning method for kernel machine by involving L1 regularized, a novel incremental learning algorithm for L1 regularized Kernel Minimum Squared Error machine (L1-KMSE-Increm) is proposed and evaluated on both synthetic and real data sets. The simulation results reveal that L1-KMSE-Increm algorithm can obtain almost the same prediction accuracy as that of corresponding batch learning method, and significantly outperforms the competitor on the sparse ratio of model and the running time.