Face recognition has been widely used and developed rapidly in recent years.The methods based on sparse representation have made great breakthroughs,and collaborative representation-based classification(CRC)is the typ...Face recognition has been widely used and developed rapidly in recent years.The methods based on sparse representation have made great breakthroughs,and collaborative representation-based classification(CRC)is the typical representative.However,CRC cannot distinguish similar samples well,leading to a wrong classification easily.As an improved method based on CRC,the two-phase test sample sparse representation(TPTSSR)removes the samples that make little contribution to the representation of the testing sample.Nevertheless,only one removal is not sufficient,since some useless samples may still be retained,along with some useful samples maybe being removed randomly.In this work,a novel classifier,called discriminative sparse parameter(DSP)classifier with iterative removal,is proposed for face recognition.The proposed DSP classifier utilizes sparse parameter to measure the representation ability of training samples straight-forward.Moreover,to avoid some useful samples being removed randomly with only one removal,DSP classifier removes most uncorrelated samples gradually with iterations.Extensive experiments on different typical poses,expressions and noisy face datasets are conducted to assess the performance of the proposed DSP classifier.The experimental results demonstrate that DSP classifier achieves a better recognition rate than the well-known SRC,CRC,RRC,RCR,SRMVS,RFSR and TPTSSR classifiers for face recognition in various situations.展开更多
Logistic regression is a fast classifier and can achieve higher accuracy on small training data.Moreover,it can work on both discrete and continuous attributes with nonlinear patterns.Based on these properties of logi...Logistic regression is a fast classifier and can achieve higher accuracy on small training data.Moreover,it can work on both discrete and continuous attributes with nonlinear patterns.Based on these properties of logistic regression,this paper proposed an algorithm,called evolutionary logistical regression classifier(ELRClass),to solve the classification of evolving data streams.This algorithm applies logistic regression repeatedly to a sliding window of samples in order to update the existing classifier,to keep this classifier if its performance is deteriorated by the reason of bursting noise,or to construct a new classifier if a major concept drift is detected.The intensive experimental results demonstrate the effectiveness of this algorithm.展开更多
基金Project(2019JJ40047)supported by the Hunan Provincial Natural Science Foundation of ChinaProject(kq2014057)supported by the Changsha Municipal Natural Science Foundation,China。
文摘Face recognition has been widely used and developed rapidly in recent years.The methods based on sparse representation have made great breakthroughs,and collaborative representation-based classification(CRC)is the typical representative.However,CRC cannot distinguish similar samples well,leading to a wrong classification easily.As an improved method based on CRC,the two-phase test sample sparse representation(TPTSSR)removes the samples that make little contribution to the representation of the testing sample.Nevertheless,only one removal is not sufficient,since some useless samples may still be retained,along with some useful samples maybe being removed randomly.In this work,a novel classifier,called discriminative sparse parameter(DSP)classifier with iterative removal,is proposed for face recognition.The proposed DSP classifier utilizes sparse parameter to measure the representation ability of training samples straight-forward.Moreover,to avoid some useful samples being removed randomly with only one removal,DSP classifier removes most uncorrelated samples gradually with iterations.Extensive experiments on different typical poses,expressions and noisy face datasets are conducted to assess the performance of the proposed DSP classifier.The experimental results demonstrate that DSP classifier achieves a better recognition rate than the well-known SRC,CRC,RRC,RCR,SRMVS,RFSR and TPTSSR classifiers for face recognition in various situations.
文摘Logistic regression is a fast classifier and can achieve higher accuracy on small training data.Moreover,it can work on both discrete and continuous attributes with nonlinear patterns.Based on these properties of logistic regression,this paper proposed an algorithm,called evolutionary logistical regression classifier(ELRClass),to solve the classification of evolving data streams.This algorithm applies logistic regression repeatedly to a sliding window of samples in order to update the existing classifier,to keep this classifier if its performance is deteriorated by the reason of bursting noise,or to construct a new classifier if a major concept drift is detected.The intensive experimental results demonstrate the effectiveness of this algorithm.
