Kernel principal component analysis based topic tracking system
Wu Guo · Journal of Tsinghua University(Science and Technology) · 2013
Topic tracking is important in information processing with robust feature extraction as a key research point.This paper describes a topic tracking system based on kernel principal component analysis(KPCA) to resolve the topic drift problem.The algorithm first computes a weighted matrix using topic prior knowledge in the development set.The KPCA based algorithm is then used for each topic sample to compensate for drift and to enhance the robustness of the sample features.Finally,the K-nearest neighbor(KNN) and Rocchio methods are used as classifiers to track each topic sample.Tests using the Fisher English transcript corpus show that this system reduces the detection cost by 15%-18% compared with the baseline system.