An efficient discriminant-based solution for small sample size problem
An efficient discriminant-based solution for small sample size problem
复制标题
DOI：
10.1016/j.patcog.2008.08.036
复制
发表时间：
2009-05-01
影响因子：
8
通讯作者：
Nenadic, Zoran
中科院分区：
文献类型：
--
作者：
Das, Koel;Nenadic, Zoran
由于潜在的小样本问题，高维统计数据的分类通常不适合标准的模式识别技术。为了解决有限样本下的高维数据分类问题，提出了一种基于主成分分析（PCA）的特征提取/分类方法.所提出的方法产生一个分段线性特征子空间，特别适合于难以识别的问题，可实现的分类率本质上是低的。在类高度重叠的情况下，或者在数据中突出的曲率使得投影到单个线性子空间上不充分的情况下，经常会遇到这样的问题。所提出的特征提取/分类方法使用类相关PCA结合线性判别特征提取，并在各种现实世界的数据集上表现良好，从数字识别到高维生物信息学和脑成像数据的分类。(C)2008爱思唯尔有限公司版权所有
Classification of high-dimensional statistical data is usually not amenable to standard pattern recognition techniques because of an underlying small sample size problem. To address the problem of high-dimensional data classification in the face of a limited number of samples, a novel principal component analysis (PCA) based feature extraction/classification scheme is proposed. The proposed method yields a piecewise linear feature subspace and is particularly well-suited to difficult recognition problems where achievable classification rates are intrinsically low. Such problems are often encountered in cases where classes are highly overlapped, or in cases where a prominent Curvature in data renders a projection onto a single linear subspace inadequate. The proposed feature extraction/classification method uses class-dependent PCA in Conjunction With linear discriminant feature extraction and performs well on a variety of real-world datasets, ranging from digit recognition to classification of high-dimensional bioinformatics and brain imaging data. (C) 2008 Elsevier Ltd. All rights reserved