南京理工大学学报（自然科学版）2018，Vol.42Issue(3)：286-291,6.DOI:10.14177/j.cnki.32-1397n.2018.42.03.005
基于主元分析和线性判别分析降维的稀疏表示分类
Sparse representation-based classification based on principal component analysis and linear discriminant analysis dimensionality reduction
摘要
Abstract
Two sparse representation-based classification(SRC)algorithms based on dimensionality reduction are proposed to solve the high time cost problem of traditional sparse representation-based classification methods for small sample data face recognition. In the extended principal component analysis(EPCA)algorithm,an optimization sparse model is achieved using the PCA algorithm,the test samples are represented linearly,and classification is performed by comparing the reconstructed PCA coefficient of the test samples with that of training samples. In the EPCA+linear discriminant analysis(EPCA+LDA) algorithm,a LDA constraint model is added to improve the identification of sparse representation of reconstructed samples. The experimental results of the AR and FERET database show that, compared with extended SRC ( ESRC ), SRC, SRC _ PCA, collaborative representation-based classification ( CRC ) algorithm, the algorithms proposed here have higher recognition rates and lower time complexities.Especially on the FERET database,the EPCA algorithm and EPCA+LDA algorithm achieve 61.46% and 59.17% recognition rates,and 383.02 s and 220.62 s running times respectively.关键词
主元分析/线性判别分析/降维/稀疏表示分类/人脸识别/协同表达分类Key words
principal component analysis/linear discriminant analysis/dimensionality reduction/sparse representation-based classification/face recognition/collaborative representation-based classification分类
信息技术与安全科学引用本文复制引用
那天,宋晓宁,於东军..基于主元分析和线性判别分析降维的稀疏表示分类[J].南京理工大学学报（自然科学版）,2018,42(3):286-291,6.基金项目
国家重点研发计划(2017YFC1601800) （2017YFC1601800）
中国博士后科学基金(2018T110441) （2018T110441）
江苏省自然科学基金(BK20161135) （BK20161135）
江苏省"六大人才高峰"高层次人才项目(XYDXX-012) （XYDXX-012）
