Feature Generation by Simple-FLDA for Pattern Recognition
Feature Generation by Simple-FLDA for Pattern Recognition
复制标题
用于模式识别的 Simple-FLDA 特征生成
DOI：
10.1109/cimca.2005.1631555
复制
发表时间：
2005
期刊：
影响因子：
--
通讯作者：
Y. Mitsukura
中科院分区：
文献类型：
--
作者：
M. Fukumi;Y. Mitsukura
本文提出了一种新的模式识别特征生成方法，该方法是从Fisher线性判别分析（FLDA）的几何解释中近似推导出来的。在模式识别或信号处理领域，主成分分析（PCA）是数据压缩和特征提取的常用方法。此外，在包括神经网络在内的这些领域中，已经提出了用于在PCA中获得特征向量的迭代学习算法。其有效性已在许多应用中得到证明。然而，近年来，FLDA已被用于许多领域，特别是人脸图像分析。FLDA的缺点是计算时间长，基于大尺寸的协方差矩阵和类内协方差矩阵通常是奇异的问题。通常，FLDA必须执行类内方差的最小化。然而，在这种情况下，不能获得类内协方差矩阵的逆矩阵，因为数据维度通常高于数据的数量，并且它包括许多零特征值。为了克服这一困难，本文提出了一种新的迭代特征生成方法--简单FLDA，并证明了它在模式识别问题中的有效性
In this paper, a new feature generation method for pattern recognition is proposed, which is approximately derived from geometrical interpretation of the Fisher linear discriminant analysis (FLDA). In a field of pattern recognition or signal processing, the principal component analysis (PCA) is popular for data compression and feature extraction. Furthermore, iterative learning algorithms for obtaining eigenvectors in PCA have been presented in such fields, including neural networks. Their effectiveness has been demonstrated in many applications. However, recently the FLDA has been used in many fields, especially face image analysis. The drawback of FLDA is a long computational time based on a large-sized covariance matrix and the issue that the within-class covariance matrix is usually singular. Generally FLDA has to carry out minimization of a within-class variance. However in this case the inverse matrix of the within-class covariance matrix cannot be obtained, since data dimension is generally higher than the number of data and then it includes many zero eigenvalues. In order to overcome this difficulty, a new iterative feature generation method, a simple FLDA is introduced and its effectiveness is demonstrated for pattern recognition problems
基于特征脸的人脸建模与识别
DOI：
--
发表时间：
2003
期刊：
IPSJ SIG Technical Reports Vol. CVIM-139
影响因子：
--
作者：
T.;Shakunaga;F.;Sakaue;Y.;Matsubara
通讯作者：
Matsubara