|Table of Contents|

Sparse representation-based classification based on principal component analysis and linear discriminant analysis dimensionality reduction(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2018年03期
Page:
286-
Research Field:
Publishing date:

Info

Title:
Sparse representation-based classification based on principal component analysis and linear discriminant analysis dimensionality reduction
Author(s):
Na Tian1Song Xiaoning1Yu Dongjun2
1.School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China; 2.School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
Keywords:
principal component analysis linear discriminant analysis dimensionality reduction sparse representation-based classification face recognition collaborative representation-based classification
PACS:
TP391
DOI:
10.14177/j.cnki.32-1397n.2018.42.03.005
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.

References:

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Last Update: 2018-06-30