Complex-valued kernel incremental metalearning algorithms
Yan Ye, Chunuang Li · 2014
Metalearning algorithm learns the base learning algorithm, thus to improve the performance of the learning system. Usually, metalearning algorithm exhibits faster convergence rate and lower mean-square error (MSE) than the original base learning algorithm. The Kernel method is a powerful tool for extending an algorithm from linear to nonlinear case. In a previous work, we have presented a kernel incremental metalearning algorithm (KIMEL). In recent years, complex-valued signal processing algorithms are gaining popularity due to their broad applicability. In this paper, we present complex-valued KIMEL (CKMIEL), which has two versions. One is based on the complexification of the real RKHS, named CKIMEL1, while the other uses a pure complex kernel, named CKIMEL2. To demonstrate the effectiveness and advantage of the proposed algorithms, we apply them to nonlinear channel identification. Experimental results show that the CKIMEL algorithms have fast convergence rate and low convergence MSE, and the CKIMEL1 algorithm is superior to the competing algorithms.