摘要
对高维数据进行判别分析,典型的策略包含数据压缩、特征提取与特征选择三步．该文对于选择合适的特征进行判别分析提出了一个定理,并应用这个定理对常用的主成分判别方法作了改进．最后,作者把改进的方法与两种常用的方法应用于一个神经生理试验数据的判别分析．结果表明,在保证判别能力的同时,改进后的方法下用于判别的特征减少了．
Motivated by a real life example from neuroscience, the authors present a theoretical frame for feature selection in discriminant analysis of very high-dimensional data. In light of a theorem, the authors provide a modification to a procedure, which is commonly-employed, of discriminant analysis of very high-dimensional data. The modified procedure works are better than two other popular procedures in this example in that it needs fewer features and the classification error is smaller.
出处
《数学物理学报（A辑）》
CSCD
北大核心
2006年第5期647-652,共6页
Acta Mathematica Scientia
基金
国家自然科学基金(NSSF10171051)资助
关键词
判别分析
高维数据
主成分分析
离散小波变换
最优特征子集
Discrete wavelet transformation
Discriminant analysis
High-dimensional data
Optimal subsets of features
Principal component analysis
