基于高斯分布监督学习样本的Fisher核构造方法

结合实际应用背景, 针对各类样本服从高斯分布的监督学习情形, 提出了构造Fisher核的新方法. 由于利用了样本中的类别信息, 该方法用极大似然估计代替EM算法估计GMM参数, 有效降低了Fisher核构造的时间复杂度. 结合核Fisher分类法, 上述方法在标准人脸库上的仿真实验结果显示, 用所提方法所构造的Fisher核不仅时间复杂度低, 且识别率也优于传统的高斯核与多项式核. 本文的研究有利于将Fisher 核的应用从语音识别领域拓展到图像识别等领域.

Fisher Kernel Construction Method Based on Gaussian Distribution

Based on the actual application background and the supervised learning situation in which samples of each class comply with Gaussian distribution, we propose a new method for Fisher kernel construction. With the help of classification information in the sample, this method use maximum likelihood estimation rather than EM algorithm to estimate the GMM parameters, which can effectively reduce the time complexity for Fisher kernel construction. A simulation experiment on standard face database shows that the above-mentioned method combined with Fisher kernel classification can not only reduce the time complexity of fisher kernel construction, but also exceed the traditional Gaussian kernel and polynomial kernel in terms of recognition rate. The study of this method will benefit the application of Fisher kernel from speech recognition to image recognition.