摘要
通过PCA方法来提取人脸特征,这些特征进一步映射到Fisher最优子空间,在这个子空间,类间分布同类内分布的比率最大。然后,提出一种新颖的有监督的聚类方法,利用有限的训练数据信息来选择RBF的结构和初始参数。最后,提出了一种混合的学习算法来训练RBF神经网络,使得在梯度下降寻优算法中大大降低了搜索空间的维数。在ORL数据库上进行的仿真结果表明,这个方法无论是在分类的错误率上还是在学习的效率上都能表现出极好的性能。
This paper uses the PCA method to extract facial features further mapped to the Fisher optimal sub-space,in this sub-space,between-class distribution within the same distribution ratio of the maximum,then puts forward a novel supervised clustering method,uses limited training data information to select the RBF structure and initial parameters.Finally,a hybrid learning algorithm to train the RBF neural networks,makes the gradient descent optimization algorithm greatly reduce the search space dimension.In ORL database the simulation results demonstrate that this method both in the classification error rate or the efficiency in the study can show excellent performance.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第16期204-207,共4页
Computer Engineering and Applications
关键词
神经网络
径向基
主元分析法
线性判别式
neural networks
radial basis
principal component analysis
linear discriminant
