signal manifold by extended kernel method and its application

点击翻译按钮获取中文摘要

In order to improve the performance of signal processing and pattern recognition, it is necessary to obtain a proper metric for signals and patterns and develop processing and recognition methods based on the metric.According to the concept, we improve the theory of Mahalanobis metric, develop a calculation method of Mahalanobis metric using kernel functions, and provide criteria and calculation methods that can obtain a equivalent result with the maximum posteriori method without estimating a posteriori probability. We also propose a method to directly estimate the L2 distance between two probability density functions and show its properties with respect to convergence and bias. We develop an algorithm to calculate the sample average on Stiefel manifold, and the adaptive kernel principle component analysis that can extract features with a small number of basis functions. The advantages of developed methods are shown by experimental results.