An algorithm for unsupervised linear discriminant analysis was presented. Optimal unsupervised discriminant vectors are obtained through maximizing covariance of all samples and minimizing covariance of local k-neares...An algorithm for unsupervised linear discriminant analysis was presented. Optimal unsupervised discriminant vectors are obtained through maximizing covariance of all samples and minimizing covariance of local k-nearest neighbor samples. The experimental results show our algorithm is effective.展开更多
Marginal Fisher analysis (MFA) not only aims to maintain the original relations of neighboring data points of the same class but also wants to keep away neighboring data points of the different classes.MFA can effec...Marginal Fisher analysis (MFA) not only aims to maintain the original relations of neighboring data points of the same class but also wants to keep away neighboring data points of the different classes.MFA can effectively overcome the limitation of linear discriminant analysis (LDA) due to data distribution assumption and available projection directions.However,MFA confronts the undersampled problems.Generalized marginal Fisher analysis (GMFA) based on a new optimization criterion is presented,which is applicable to the undersampled problems.The solutions to the proposed criterion for GMFA are derived,which can be characterized in a closed form.Among the solutions,two specific algorithms,namely,normal MFA (NMFA) and orthogonal MFA (OMFA),are studied,and the methods to implement NMFA and OMFA are proposed.A comparative study on the undersampled problem of face recognition is conducted to evaluate NMFA and OMFA in terms of classification accuracy,which demonstrates the effectiveness of the proposed algorithms.展开更多
In computer vision,emotion recognition using facial expression images is considered an important research issue.Deep learning advances in recent years have aided in attaining improved results in this issue.According t...In computer vision,emotion recognition using facial expression images is considered an important research issue.Deep learning advances in recent years have aided in attaining improved results in this issue.According to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of emotion.It is feasible and useful to convert face photos into collections of visual words and carry out global expression recognition.The main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is used.AffectNet uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos online.The FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization phase.Linear discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously enhanced.Grid search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.展开更多
In this paper, a multi-view gait based human recognition system using the fusion of two kinds of features is proposed.We use cross wavelet transform to extract dynamic feature and bipartite graph model to extract stat...In this paper, a multi-view gait based human recognition system using the fusion of two kinds of features is proposed.We use cross wavelet transform to extract dynamic feature and bipartite graph model to extract static feature which are coefficients of quadrature mirror filter(QMF)-graph wavelet filter bank. Feature fusion is done after normalization. For normalization of features, min-max rule is used and mean-variance method is used to find weights for normalized features. Euclidean distance between each feature vector and center of the cluster which is obtained by k-means clustering is used as similarity measure in Bayesian framework. Experiments performed on widely used CASIA B gait database show that, the fusion of these two feature sets preserve discriminant information. We report 99.90 % average recognition rate.展开更多
High-grade squamous intraepithelial lesion(HSIL)is regarded as a serious precancerous state of cervix,and it is easy to progress into cervical invasive carcinoma which highlights the importance of earlier diagnosis an...High-grade squamous intraepithelial lesion(HSIL)is regarded as a serious precancerous state of cervix,and it is easy to progress into cervical invasive carcinoma which highlights the importance of earlier diagnosis and treatment of cervical lesions.Pathologists examine the biopsied cervical epithelial tissue through a microscope.The pathological examination will take a long time and sometimes results in high inter-and intra-observer variability in outcomes.Polarization imaging techniques have broad application prospects for biomedical diagnosis such as breast,liver,colon,thyroid and so on.In our team,we have derived polarimetry feature parameters(PFPs)to characterize microstructural features in histological sections of breast tissues,and the accuracy for PFPs ranges from 0.82 to 0.91.Therefore,the aim of this paper is to distinguish automatically microstructural features between HSIL and cervical squamous cell carcinoma(CSCC)by means of polarization imaging techniques,and try to provide quantitative reference index for patho-logical diagnosis which can alleviate the workload of pathologists.Polarization images of the H&E stained histological slices were obtained by Mueller matrix microscope.The typical path-ological structure area was labeled by two experienced pathologists.Calculate the polarimetry basis parameter(PBP)statistics for this region.The PBP statistics(stat PBPs)are screened by mutual information(MI)method.The training method is based on a linear discriminant analysis(LDA)classier whichnds the most simplied linear combination from these stat PBPs and the accuracy remains constant to characterize the specic microstructural feature quantitatively in cervical squamous epithelium.We present results from 37 clinical patients with analysis regions of cervical squamous epithelium.The accuracy of PFP for recognizing HSIL and CSCC was 83.8%and 87.5%,respectively.This work demonstrates the ability of PFP to quantitatively charac-terize the cervical squamous epithelial lesions in the H&E pathological sections.Signicance:Polarization detection technology provides an effcient method for digital pathological diagnosis and points out a new way for automatic screening of pathological sections.展开更多
Objective:Poria cocos and Polyporus umbellatus are similar medicinal fungi in traditional Chinese medicines.A method for fingerprint analysis of monosaccharide composition of polysaccharides by HPLC combined with chem...Objective:Poria cocos and Polyporus umbellatus are similar medicinal fungi in traditional Chinese medicines.A method for fingerprint analysis of monosaccharide composition of polysaccharides by HPLC combined with chemometrics methods has been developed for characterization and discrimination of them in this research.Methods:The polysaccharides were extracted by decocting in water,and then completely hydrolyzed with hydrochloride.Monosaccharides in the hydrolyzates were derivatized with 1-phenyl-3-methyl-5-pyrazolone(PMP)for HPLC analysis.More than 20 batches of P.cocos and P.umbellatus from different regions were analyzed.Results:The fingerprints of P.cocos showed five common characteristic peaks,which were identified by comparing with the reference substances.The five peaks corresponded to the derivatives of mannose,ribose,glucose,galactose,and fucose.At the same time,the fingerprints of P.umbellatus showed eight common characteristic peaks,of which seven were identified as the derivatives of mannose,ribose,rhamnose,glucose,galactose,xylose,and fucose.Moreover,the similarity of their fingerprints was respectively calculated by the Similarity Evaluation System for Chromatographic Fingerprint of TCM published by China Pharmacopoeia Committee(Version 2004 A).And the data were further processed by hierarchical cluster analysis(HCA)and principal component analysis(PCA).The similarity evaluation and HCA indicated that there were no significant difference in P.cocos or P.umbellatus samples from different geographical regions,but PCA was performed to characterize the difference in monosaccharide constituents between P.cocos and P.umbellatus,and linear discriminant analysis(LDA)showed the overall correct classification rate was 100%.Conclusion:The fingerprint analysis method of monosaccharide composition of water-soluble polysaccharides can distinguish P.cocos and P.umbellatus,and can be applied for the authentication or quality control for P.cocos and P.umbellatus.展开更多
Diagnosis of the Graves’ophthalmology remains a significant challenge.We identified between Graves’ophthalmology tissues and healthy controls by using laser-induced breakdown spectroscopy(LIBS)combined with machine ...Diagnosis of the Graves’ophthalmology remains a significant challenge.We identified between Graves’ophthalmology tissues and healthy controls by using laser-induced breakdown spectroscopy(LIBS)combined with machine learning method.In this work,the paraffin-embedded samples of the Graves’ophthalmology were prepared for LIBS spectra acquisition.The metallic elements(Na,K,Al,Ca),non-metallic element(O)and molecular bands((C-N),(C-O))were selected for diagnosing Graves’ophthalmology.The selected spectral lines were inputted into the supervised classification methods including linear discriminant analysis(LDA),support vector machine(SVM),k-nearest neighbor(ANN),and generalized regression neural network(GRNN),respectively.The results showed that the predicted accuracy rates of LDA,SVM,ANN,GRNN were 76.33%,96.28%,96.56%,and 96.33%,respectively.The sensitivity of four models were 75.89%,93.78%,96.78%,and 96.67%,respectively.The specificity of four models were 76.78%,98.78%,96.33%,and 96.00%,respectively.This demonstrated that LIBS assisted with a nonlinear model can be used to identify Graves’ophthalmopathy with a higher rate of accuracy.The ANN had the best performance by comparing the three nonlinear models.Therefore,LIBS combined with machine learning method can be an effective way to discriminate Graves’ophthalmology.展开更多
A new set of descriptors,namely score vectors of the zero dimension,one dimension,two dimensions and three dimensions(SZOTT),was derived from principle component analysis of a matrix of 1369 structural variables inclu...A new set of descriptors,namely score vectors of the zero dimension,one dimension,two dimensions and three dimensions(SZOTT),was derived from principle component analysis of a matrix of 1369 structural variables including 0D,1D,2D and 3D information for the 20 coded amino acids. SZOTT scales were then used in cleavage site prediction of human immunodeficiency virus type 1 protease. Linear discriminant analysis(LDA) and support vector machines(SVM) were applied to developing models to predict the cleavage sites. The results obtained by linear discriminant analysis(LDA) and support vector machines(SVM) are as follows. The Matthews correlation coefficients(MCC) by the resubstitution test,leave-one-out cross validation(LOOCV) and external validation are 0.879 and 0.911,0.849 and 0.901,0.822 and 0.846,respectively. The receiver operating characteristic(ROC) analysis showed that the SVM model possesses better simulative and predictive ability in comparison with the LDA model. Satisfactory results show that SZOTT descriptors can be further used to predict cleavage sites of human immunodeficiency virus type 1 protease.展开更多
This paper proposes a new cost-efficient,adaptive,and self-healing algorithm in real time that detects faults in a short period with high accuracy,even in the situations when it is difficult to detect.Rather than usin...This paper proposes a new cost-efficient,adaptive,and self-healing algorithm in real time that detects faults in a short period with high accuracy,even in the situations when it is difficult to detect.Rather than using traditional machine learning(ML)algorithms or hybrid signal processing techniques,a new framework based on an optimization enabled weighted ensemble method is developed that combines essential ML algorithms.In the proposed method,the system will select and compound appropriate ML algorithms based on Particle Swarm Optimization(PSO)weights.For this purpose,power system failures are simulated by using the PSCA D-Python co-simulation.One of the salient features of this study is that the proposed solution works on real-time raw data without using any pre-computational techniques or pre-stored information.Therefore,the proposed technique will be able to work on different systems,topologies,or data collections.The proposed fault detection technique is validated by using PSCAD-Python co-simulation on a modified and standard IEEE-14 and standard IEEE-39 bus considering network faults which are difficult to detect.展开更多
Searching,recognizing and retrieving a video of interest froma large collection of a video data is an instantaneous requirement.This requirement has been recognized as an active area of research in computer vision,mac...Searching,recognizing and retrieving a video of interest froma large collection of a video data is an instantaneous requirement.This requirement has been recognized as an active area of research in computer vision,machine learning and pattern recognition.Flower video recognition and retrieval is vital in the field of floriculture and horticulture.In this paper we propose a model for the retrieval of videos of flowers.Initially,videos are represented with keyframes and flowers in keyframes are segmented from their background.Then,the model is analysed by features extracted from flower regions of the keyframe.A Linear Discriminant Analysis(LDA)is adapted for the extraction of discriminating features.Multiclass Support VectorMachine(MSVM)classifier is applied to identify the class of the query video.Experiments have been conducted on relatively large dataset of our own,consisting of 7788 videos of 30 different species of flowers captured from three different devices.Generally,retrieval of flower videos is addressed by the use of a query video consisting of a flower of a single species.In this work we made an attempt to develop a system consisting of retrieval of similar videos for a query video consisting of flowers of different species.展开更多
This paper introduces an idea of generating a kernel from an arbitrary function by embedding the training samples into the function.Based on this idea,we present two nonlinear feature extraction methods:generating ker...This paper introduces an idea of generating a kernel from an arbitrary function by embedding the training samples into the function.Based on this idea,we present two nonlinear feature extraction methods:generating kernel principal component analysis(GKPCA)and generating kernel Fisher discriminant(GKFD).These two methods are shown to be equivalent to the function-mapping-space PCA(FMS-PCA)and the function-mapping-space linear discriminant analysis(FMS-LDA)methods,respectively.This equivalence reveals that the generating kernel is actually determined by the corresponding function map.From the generating kernel point of view,we can classify the current kernel Fisher discriminant(KFD)algorithms into two categories:KPCA+LDA based algorithms and straightforward KFD(SKFD)algorithms.The KPCA+LDA based algorithms directly work on the given kernel and are not suitable for non-kernel functions,while the SKFD algorithms essentially work on the generating kernel from a given symmetric function and are therefore suitable for non-kernels as well as kernels.Finally,we outline the tensor-based feature extraction methods and discuss ways of extending tensor-based methods to their generating kernel versions.展开更多
文摘An algorithm for unsupervised linear discriminant analysis was presented. Optimal unsupervised discriminant vectors are obtained through maximizing covariance of all samples and minimizing covariance of local k-nearest neighbor samples. The experimental results show our algorithm is effective.
基金supported by Science Foundation of the Fujian Province of China (No. 2010J05099)
文摘Marginal Fisher analysis (MFA) not only aims to maintain the original relations of neighboring data points of the same class but also wants to keep away neighboring data points of the different classes.MFA can effectively overcome the limitation of linear discriminant analysis (LDA) due to data distribution assumption and available projection directions.However,MFA confronts the undersampled problems.Generalized marginal Fisher analysis (GMFA) based on a new optimization criterion is presented,which is applicable to the undersampled problems.The solutions to the proposed criterion for GMFA are derived,which can be characterized in a closed form.Among the solutions,two specific algorithms,namely,normal MFA (NMFA) and orthogonal MFA (OMFA),are studied,and the methods to implement NMFA and OMFA are proposed.A comparative study on the undersampled problem of face recognition is conducted to evaluate NMFA and OMFA in terms of classification accuracy,which demonstrates the effectiveness of the proposed algorithms.
文摘In computer vision,emotion recognition using facial expression images is considered an important research issue.Deep learning advances in recent years have aided in attaining improved results in this issue.According to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of emotion.It is feasible and useful to convert face photos into collections of visual words and carry out global expression recognition.The main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is used.AffectNet uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos online.The FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization phase.Linear discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously enhanced.Grid search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.
文摘In this paper, a multi-view gait based human recognition system using the fusion of two kinds of features is proposed.We use cross wavelet transform to extract dynamic feature and bipartite graph model to extract static feature which are coefficients of quadrature mirror filter(QMF)-graph wavelet filter bank. Feature fusion is done after normalization. For normalization of features, min-max rule is used and mean-variance method is used to find weights for normalized features. Euclidean distance between each feature vector and center of the cluster which is obtained by k-means clustering is used as similarity measure in Bayesian framework. Experiments performed on widely used CASIA B gait database show that, the fusion of these two feature sets preserve discriminant information. We report 99.90 % average recognition rate.
基金the Guangming District Economic Development Special Fund(2020R01043)。
文摘High-grade squamous intraepithelial lesion(HSIL)is regarded as a serious precancerous state of cervix,and it is easy to progress into cervical invasive carcinoma which highlights the importance of earlier diagnosis and treatment of cervical lesions.Pathologists examine the biopsied cervical epithelial tissue through a microscope.The pathological examination will take a long time and sometimes results in high inter-and intra-observer variability in outcomes.Polarization imaging techniques have broad application prospects for biomedical diagnosis such as breast,liver,colon,thyroid and so on.In our team,we have derived polarimetry feature parameters(PFPs)to characterize microstructural features in histological sections of breast tissues,and the accuracy for PFPs ranges from 0.82 to 0.91.Therefore,the aim of this paper is to distinguish automatically microstructural features between HSIL and cervical squamous cell carcinoma(CSCC)by means of polarization imaging techniques,and try to provide quantitative reference index for patho-logical diagnosis which can alleviate the workload of pathologists.Polarization images of the H&E stained histological slices were obtained by Mueller matrix microscope.The typical path-ological structure area was labeled by two experienced pathologists.Calculate the polarimetry basis parameter(PBP)statistics for this region.The PBP statistics(stat PBPs)are screened by mutual information(MI)method.The training method is based on a linear discriminant analysis(LDA)classier whichnds the most simplied linear combination from these stat PBPs and the accuracy remains constant to characterize the specic microstructural feature quantitatively in cervical squamous epithelium.We present results from 37 clinical patients with analysis regions of cervical squamous epithelium.The accuracy of PFP for recognizing HSIL and CSCC was 83.8%and 87.5%,respectively.This work demonstrates the ability of PFP to quantitatively charac-terize the cervical squamous epithelial lesions in the H&E pathological sections.Signicance:Polarization detection technology provides an effcient method for digital pathological diagnosis and points out a new way for automatic screening of pathological sections.
基金supported by Shanghai Biotechnology Support Project(Grant No.16401900800)National Science and Technology Project(Grant No.2018ZX09721003-009-011).
文摘Objective:Poria cocos and Polyporus umbellatus are similar medicinal fungi in traditional Chinese medicines.A method for fingerprint analysis of monosaccharide composition of polysaccharides by HPLC combined with chemometrics methods has been developed for characterization and discrimination of them in this research.Methods:The polysaccharides were extracted by decocting in water,and then completely hydrolyzed with hydrochloride.Monosaccharides in the hydrolyzates were derivatized with 1-phenyl-3-methyl-5-pyrazolone(PMP)for HPLC analysis.More than 20 batches of P.cocos and P.umbellatus from different regions were analyzed.Results:The fingerprints of P.cocos showed five common characteristic peaks,which were identified by comparing with the reference substances.The five peaks corresponded to the derivatives of mannose,ribose,glucose,galactose,and fucose.At the same time,the fingerprints of P.umbellatus showed eight common characteristic peaks,of which seven were identified as the derivatives of mannose,ribose,rhamnose,glucose,galactose,xylose,and fucose.Moreover,the similarity of their fingerprints was respectively calculated by the Similarity Evaluation System for Chromatographic Fingerprint of TCM published by China Pharmacopoeia Committee(Version 2004 A).And the data were further processed by hierarchical cluster analysis(HCA)and principal component analysis(PCA).The similarity evaluation and HCA indicated that there were no significant difference in P.cocos or P.umbellatus samples from different geographical regions,but PCA was performed to characterize the difference in monosaccharide constituents between P.cocos and P.umbellatus,and linear discriminant analysis(LDA)showed the overall correct classification rate was 100%.Conclusion:The fingerprint analysis method of monosaccharide composition of water-soluble polysaccharides can distinguish P.cocos and P.umbellatus,and can be applied for the authentication or quality control for P.cocos and P.umbellatus.
基金This work was supported by the National Natural Science Foundation of China(Grant No.61575073)The authors would also like to acknowledge valuable discussions with the master student Haohao Cui.
文摘Diagnosis of the Graves’ophthalmology remains a significant challenge.We identified between Graves’ophthalmology tissues and healthy controls by using laser-induced breakdown spectroscopy(LIBS)combined with machine learning method.In this work,the paraffin-embedded samples of the Graves’ophthalmology were prepared for LIBS spectra acquisition.The metallic elements(Na,K,Al,Ca),non-metallic element(O)and molecular bands((C-N),(C-O))were selected for diagnosing Graves’ophthalmology.The selected spectral lines were inputted into the supervised classification methods including linear discriminant analysis(LDA),support vector machine(SVM),k-nearest neighbor(ANN),and generalized regression neural network(GRNN),respectively.The results showed that the predicted accuracy rates of LDA,SVM,ANN,GRNN were 76.33%,96.28%,96.56%,and 96.33%,respectively.The sensitivity of four models were 75.89%,93.78%,96.78%,and 96.67%,respectively.The specificity of four models were 76.78%,98.78%,96.33%,and 96.00%,respectively.This demonstrated that LIBS assisted with a nonlinear model can be used to identify Graves’ophthalmopathy with a higher rate of accuracy.The ANN had the best performance by comparing the three nonlinear models.Therefore,LIBS combined with machine learning method can be an effective way to discriminate Graves’ophthalmology.
基金Supported by the Research on National High-tech R&D Program (the 863 program) (Grant No. 2006AA02Z312)Innovative Group Program for Graduates of Chong- qing University, Science and Innovation Fund (Grant No. 200711C1A0010260)
文摘A new set of descriptors,namely score vectors of the zero dimension,one dimension,two dimensions and three dimensions(SZOTT),was derived from principle component analysis of a matrix of 1369 structural variables including 0D,1D,2D and 3D information for the 20 coded amino acids. SZOTT scales were then used in cleavage site prediction of human immunodeficiency virus type 1 protease. Linear discriminant analysis(LDA) and support vector machines(SVM) were applied to developing models to predict the cleavage sites. The results obtained by linear discriminant analysis(LDA) and support vector machines(SVM) are as follows. The Matthews correlation coefficients(MCC) by the resubstitution test,leave-one-out cross validation(LOOCV) and external validation are 0.879 and 0.911,0.849 and 0.901,0.822 and 0.846,respectively. The receiver operating characteristic(ROC) analysis showed that the SVM model possesses better simulative and predictive ability in comparison with the LDA model. Satisfactory results show that SZOTT descriptors can be further used to predict cleavage sites of human immunodeficiency virus type 1 protease.
文摘This paper proposes a new cost-efficient,adaptive,and self-healing algorithm in real time that detects faults in a short period with high accuracy,even in the situations when it is difficult to detect.Rather than using traditional machine learning(ML)algorithms or hybrid signal processing techniques,a new framework based on an optimization enabled weighted ensemble method is developed that combines essential ML algorithms.In the proposed method,the system will select and compound appropriate ML algorithms based on Particle Swarm Optimization(PSO)weights.For this purpose,power system failures are simulated by using the PSCA D-Python co-simulation.One of the salient features of this study is that the proposed solution works on real-time raw data without using any pre-computational techniques or pre-stored information.Therefore,the proposed technique will be able to work on different systems,topologies,or data collections.The proposed fault detection technique is validated by using PSCAD-Python co-simulation on a modified and standard IEEE-14 and standard IEEE-39 bus considering network faults which are difficult to detect.
文摘Searching,recognizing and retrieving a video of interest froma large collection of a video data is an instantaneous requirement.This requirement has been recognized as an active area of research in computer vision,machine learning and pattern recognition.Flower video recognition and retrieval is vital in the field of floriculture and horticulture.In this paper we propose a model for the retrieval of videos of flowers.Initially,videos are represented with keyframes and flowers in keyframes are segmented from their background.Then,the model is analysed by features extracted from flower regions of the keyframe.A Linear Discriminant Analysis(LDA)is adapted for the extraction of discriminating features.Multiclass Support VectorMachine(MSVM)classifier is applied to identify the class of the query video.Experiments have been conducted on relatively large dataset of our own,consisting of 7788 videos of 30 different species of flowers captured from three different devices.Generally,retrieval of flower videos is addressed by the use of a query video consisting of a flower of a single species.In this work we made an attempt to develop a system consisting of retrieval of similar videos for a query video consisting of flowers of different species.
基金supported by the Program for New Century Excellent Talents in University of China,the NUST Outstanding Scholar Supporting Program,and the National Natural Science Foundation of China(Grant No.60973098).
文摘This paper introduces an idea of generating a kernel from an arbitrary function by embedding the training samples into the function.Based on this idea,we present two nonlinear feature extraction methods:generating kernel principal component analysis(GKPCA)and generating kernel Fisher discriminant(GKFD).These two methods are shown to be equivalent to the function-mapping-space PCA(FMS-PCA)and the function-mapping-space linear discriminant analysis(FMS-LDA)methods,respectively.This equivalence reveals that the generating kernel is actually determined by the corresponding function map.From the generating kernel point of view,we can classify the current kernel Fisher discriminant(KFD)algorithms into two categories:KPCA+LDA based algorithms and straightforward KFD(SKFD)algorithms.The KPCA+LDA based algorithms directly work on the given kernel and are not suitable for non-kernel functions,while the SKFD algorithms essentially work on the generating kernel from a given symmetric function and are therefore suitable for non-kernels as well as kernels.Finally,we outline the tensor-based feature extraction methods and discuss ways of extending tensor-based methods to their generating kernel versions.