一种新的保类内核Fisher判别法及说话人辨别应用

在保留数据本质特征的前提下,降低数据维度是一种重要的分类预处理手段.深入分析了核Fisher判别 (KFD)方法与核化全局局部保持Fisher投影(KLFDA)方法的相互关系与优缺点,提出了一种新的基于类内特性保持的核化Fisher判别分析方法(LW-KFD).在保留KFD全局最优投影能力的同时,解决了KLFDA的过度局部保持问题,从而对重叠(离群)样本与多态分簇样本都能实现有效的分类投影.提出了快速训练算法,解决了大量训练样本时的内存溢出问题.仿真实验与说话人辨别应用表明,该方法具有很强的适应性,并提高了说话人识别率与识别速度.

Novel Local Within-class Features Preservation Kernel Fisher Discriminant Algorithm and Applied in Speaker Identification

Dimensionality reduction without losing intrinsic information on original data is an important technique for succeeding tasks such as classification. A novel local within-class features preservation kernel fisher discriminant algorithm was proposed after deeply analyzing the relationship between kernel fisher discriminant and kernel local fisher projection. I}he new method keeps the ability of KFD's global projection and solves the over-fitting of KI_FDA's local preservation problem, which can work well on overlapped or multimodal labeled data. I}he training algorithm is improved for resolving out of-memory problem when applied in large sample situation. The simulation and speaker identificanon application show that the proposed algorithm has more adaptability as well as advanced recognition rate and speed.