The Experts below are selected from a list of 293001 Experts worldwide ranked by ideXlab platform
Stephen C Strother - One of the best experts on this subject based on the ideXlab platform.
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pattern classification of fmri data applications for analysis of spatially distributed cortical networks
NeuroImage, 2014Co-Authors: Grigori Yourganov, Tanya Schmah, Marc G. Berman, Nathan W Churchill, Cheryl L. Grady, Stephen C StrotherAbstract:Abstract The field of fMRI data analysis is rapidly growing in sophistication, particularly in the domain of multivariate pattern classification. However, the interaction between the properties of the analytical model and the parameters of the BOLD signal (e.g. signal magnitude, temporal variance and functional connectivity) is still an open problem. We addressed this problem by evaluating a set of pattern classification algorithms on simulated and experimental block-design fMRI data. The set of classifiers consisted of linear and quadratic Discriminants, linear support vector machine, and linear and nonlinear Gaussian naive Bayes classifiers. For linear Discriminant, we used two methods of regularization: principal component analysis, and ridge regularization. The classifiers were used (1) to classify the volumes according to the behavioral task that was performed by the subject, and (2) to construct spatial maps that indicated the relative contribution of each voxel to classification. Our evaluation metrics were: (1) accuracy of out-of-sample classification and (2) reproducibility of spatial maps. In simulated data sets, we performed an additional evaluation of spatial maps with ROC analysis. We varied the magnitude, temporal variance and connectivity of simulated fMRI signal and identified the optimal classifier for each simulated environment. Overall, the best performers were linear and quadratic Discriminants (operating on principal components of the data matrix) and, in some rare situations, a nonlinear Gaussian naive Bayes classifier. The results from the simulated data were supported by within-subject analysis of experimental fMRI data, collected in a study of aging. This is the first study that systematically characterizes interactions between analysis model and signal parameters (such as magnitude, variance and correlation) on the performance of pattern classifiers for fMRI.
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Pattern classification of fMRI data: Applications for analysis of spatially distributed cortical networks
NeuroImage, 2014Co-Authors: Grigori Yourganov, Tanya Schmah, Marc G. Berman, Nathan W Churchill, Cheryl L. Grady, Stephen C StrotherAbstract:The field of fMRI data analysis is rapidly growing in sophistication, particularly in the domain of multivariate pattern classification. However, the interaction between the properties of the analytical model and the parameters of the BOLD signal (e.g. signal magnitude, temporal variance and functional connectivity) is still an open problem. We addressed this problem by evaluating a set of pattern classification algorithms on simulated and experimental block-design fMRI data. The set of classifiers consisted of linear and quadratic Discriminants, linear support vector machine, and linear and nonlinear Gaussian naive Bayes classifiers. For linear Discriminant, we used two methods of regularization: principal component analysis, and ridge regularization. The classifiers were used (1) to classify the volumes according to the behavioral task that was performed by the subject, and (2) to construct spatial maps that indicated the relative contribution of each voxel to classification. Our evaluation metrics were: (1) accuracy of out-of-sample classification and (2) reproducibility of spatial maps. In simulated data sets, we performed an additional evaluation of spatial maps with ROC analysis. We varied the magnitude, temporal variance and connectivity of simulated fMRI signal and identified the optimal classifier for each simulated environment. Overall, the best performers were linear and quadratic Discriminants (operating on principal components of the data matrix) and, in some rare situations, a nonlinear Gaussian naïve Bayes classifier. The results from the simulated data were supported by within-subject analysis of experimental fMRI data, collected in a study of aging. This is the first study that systematically characterizes interactions between analysis model and signal parameters (such as magnitude, variance and correlation) on the performance of pattern classifiers for fMRI. © 2014 Elsevier Inc.
Jingyu Yang - One of the best experts on this subject based on the ideXlab platform.
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from classifiers to discriminators a nearest neighbor rule induced Discriminant analysis
Pattern Recognition, 2011Co-Authors: Jian Yang, Jingyu Yang, Lei Zhang, David ZhangAbstract:The current Discriminant analysis method design is generally independent of classifiers, thus the connection between Discriminant analysis methods and classifiers is loose. This paper provides a way to design Discriminant analysis methods that are bound with classifiers. We begin with a local mean based nearest neighbor (LM-NN) classifier and use its decision rule to supervise the design of a discriminator. Therefore, the derived discriminator, called local mean based nearest neighbor Discriminant analysis (LM-NNDA), matches the LM-NN classifier optimally in theory. In contrast to that LM-NNDA is a NN classifier induced Discriminant analysis method, we further show that the classical Fisher linear Discriminant analysis (FLDA) is a minimum distance classifier (i.e. nearest Class-mean classifier) induced Discriminant analysis method. The proposed LM-NNDA method is evaluated using the CENPARMI handwritten numeral database, the NUST603 handwritten Chinese character database, the ETH80 object category database and the FERET face image database. The experimental results demonstrate the performance advantage of LM-NNDA over other feature extraction methods with respect to the LM-NN (or NN) classifier.
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Block-based two-dimensional scatter-difference Discriminant analysis and face recognition
2008 7th World Congress on Intelligent Control and Automation, 2008Co-Authors: Caikou Chen, Jingyu YangAbstract:Two-dimensional scatter-difference Discriminant analysis not only essentially avoids the small sample size problem which occurred in traditional Fisher Discriminant analysis, but also saves much computational time for feature extraction. In this paper, by analyzing the equivalence and the intrinsic essence between two-dimensional scatter-difference Discriminant analysis and traditional one-dimensional scatter-difference Discriminant analysis, a new method, called block-based two-dimensional scatter difference Discriminant analysis, is proposed. It constructs the image matrix under the mode of blocked matrices. As a result, the final Discriminant features extracted are more effective for classification. Finally, extensive experiments performed on ORL and AR face database verify the effectiveness of the proposed method.
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A Novel Nonlinear Feature Extraction and Recognition Approach Based on Improved 2D Fisherface Plus Kernel Discriminant Analysis
2008 Second International Symposium on Intelligent Information Technology Application, 2008Co-Authors: Sheng Li, David Zhang, Zhu-li Shao, Xiao-yuan Jing, Jingyu YangAbstract:A novel nonlinear feature extraction and recognition approach which is based on improved 2D Fisherface plus Kernel Discriminant analysis is proposed. We provide an improved 2D Fisherface method that designs a new strategy to select appropriate 2D principal components and Discriminant vectors, then we use 2D features to perform the Kernel Discriminant analysis. The nearest neighbor classifier with cosine distance measure is adopted in classifying the nonlinear Discriminant features. The experiments show that the proposed approach achieves better recognition results than several representative Discriminant methods.
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Orthogonalized Fisher Discriminant
Pattern Recognition, 2005Co-Authors: Fengxi Song, Jingyu YangAbstract:Abstract The Foley–Sammon Discriminant (FSD) exhibits higher performance in face recognition than the Fisher linear Discriminant due to its elimination of dependences among Discriminant vectors. But its theory is complex and the calculation is time expensive. The orthogonalized Fisher Discriminant (OFD), which also derives a set of orthogonal Discriminant vectors, is very simple and easy to implement. Experiments show that OFD is more effective and efficient than FSD in pattern recognition.
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KPCA plus LDA: a complete kernel Fisher Discriminant framework for feature extraction and recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005Co-Authors: Jian Yang, Jingyu Yang, A.f. Frangi, David ZhangAbstract:This paper examines the theory of kernel Fisher Discriminant analysis (KFD) in a Hilbert space and develops a two-phase KFD framework, i.e., kernel principal component analysis (KPCA) plus Fisher linear Discriminant analysis (LDA). This framework provides novel insights into the nature of KFD. Based on this framework, the authors propose a complete kernel Fisher Discriminant analysis (CKFD) algorithm. CKFD can be used to carry out Discriminant analysis in "double Discriminant subspaces." The fact that, it can make full use of two kinds of Discriminant information, regular and irregular, makes CKFD a more powerful discriminator. The proposed algorithm was tested and evaluated using the FERET face database and the CENPARMI handwritten numeral database. The experimental results show that CKFD outperforms other KFD algorithms.
Grigori Yourganov - One of the best experts on this subject based on the ideXlab platform.
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pattern classification of fmri data applications for analysis of spatially distributed cortical networks
NeuroImage, 2014Co-Authors: Grigori Yourganov, Tanya Schmah, Marc G. Berman, Nathan W Churchill, Cheryl L. Grady, Stephen C StrotherAbstract:Abstract The field of fMRI data analysis is rapidly growing in sophistication, particularly in the domain of multivariate pattern classification. However, the interaction between the properties of the analytical model and the parameters of the BOLD signal (e.g. signal magnitude, temporal variance and functional connectivity) is still an open problem. We addressed this problem by evaluating a set of pattern classification algorithms on simulated and experimental block-design fMRI data. The set of classifiers consisted of linear and quadratic Discriminants, linear support vector machine, and linear and nonlinear Gaussian naive Bayes classifiers. For linear Discriminant, we used two methods of regularization: principal component analysis, and ridge regularization. The classifiers were used (1) to classify the volumes according to the behavioral task that was performed by the subject, and (2) to construct spatial maps that indicated the relative contribution of each voxel to classification. Our evaluation metrics were: (1) accuracy of out-of-sample classification and (2) reproducibility of spatial maps. In simulated data sets, we performed an additional evaluation of spatial maps with ROC analysis. We varied the magnitude, temporal variance and connectivity of simulated fMRI signal and identified the optimal classifier for each simulated environment. Overall, the best performers were linear and quadratic Discriminants (operating on principal components of the data matrix) and, in some rare situations, a nonlinear Gaussian naive Bayes classifier. The results from the simulated data were supported by within-subject analysis of experimental fMRI data, collected in a study of aging. This is the first study that systematically characterizes interactions between analysis model and signal parameters (such as magnitude, variance and correlation) on the performance of pattern classifiers for fMRI.
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Pattern classification of fMRI data: Applications for analysis of spatially distributed cortical networks
NeuroImage, 2014Co-Authors: Grigori Yourganov, Tanya Schmah, Marc G. Berman, Nathan W Churchill, Cheryl L. Grady, Stephen C StrotherAbstract:The field of fMRI data analysis is rapidly growing in sophistication, particularly in the domain of multivariate pattern classification. However, the interaction between the properties of the analytical model and the parameters of the BOLD signal (e.g. signal magnitude, temporal variance and functional connectivity) is still an open problem. We addressed this problem by evaluating a set of pattern classification algorithms on simulated and experimental block-design fMRI data. The set of classifiers consisted of linear and quadratic Discriminants, linear support vector machine, and linear and nonlinear Gaussian naive Bayes classifiers. For linear Discriminant, we used two methods of regularization: principal component analysis, and ridge regularization. The classifiers were used (1) to classify the volumes according to the behavioral task that was performed by the subject, and (2) to construct spatial maps that indicated the relative contribution of each voxel to classification. Our evaluation metrics were: (1) accuracy of out-of-sample classification and (2) reproducibility of spatial maps. In simulated data sets, we performed an additional evaluation of spatial maps with ROC analysis. We varied the magnitude, temporal variance and connectivity of simulated fMRI signal and identified the optimal classifier for each simulated environment. Overall, the best performers were linear and quadratic Discriminants (operating on principal components of the data matrix) and, in some rare situations, a nonlinear Gaussian naïve Bayes classifier. The results from the simulated data were supported by within-subject analysis of experimental fMRI data, collected in a study of aging. This is the first study that systematically characterizes interactions between analysis model and signal parameters (such as magnitude, variance and correlation) on the performance of pattern classifiers for fMRI. © 2014 Elsevier Inc.
De-shuang Huang - One of the best experts on this subject based on the ideXlab platform.
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Dimensionality reduction based on minimax risk criterion for face recognition
The 2010 International Joint Conference on Neural Networks (IJCNN), 2010Co-Authors: Lei Tang, De-shuang HuangAbstract:In the field of pattern recognition and machine learning, many problems are involved in the tasks of dimensionality reduction and then classification. In this paper, we develop an efficient dimensionality reduction method named MiniRisk Supervised Discrimiant Projection (MRSDP), which extracts effective low-dimensional features for classification purpose. The proposed method utilizes Discriminant information to guide the procedure of extracting intrinsic low-dimensional features and provides a linear projection matrix. Since MRSDP is based on minimax risk criterion, it can minimize the maximal probability of misclassification in the common borders of different classes of data by contracting within-class scatter and maximizing between-class scatter. The advantage of our method is borne out by comparison with other widely used methods. In the experiments on Yale face database and ORL face database, our method achieves constantly superior performance than those competing methods.
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IJCNN - Dimensionality reduction based on minimax risk criterion for face recognition
The 2010 International Joint Conference on Neural Networks (IJCNN), 2010Co-Authors: Lei Tang, De-shuang HuangAbstract:In the field of pattern recognition and machine learning, many problems are involved in the tasks of dimensionality reduction and then classification. In this paper, we develop an efficient dimensionality reduction method named MiniRisk Supervised Discrimiant Projection (MRSDP), which extracts effective low-dimensional features for classification purpose. The proposed method utilizes Discriminant information to guide the procedure of extracting intrinsic low-dimensional features and provides a linear projection matrix. Since MRSDP is based on minimax risk criterion, it can minimize the maximal probability of misclassification in the common borders of different classes of data by contracting within-class scatter and maximizing between-class scatter. The advantage of our method is borne out by comparison with other widely used methods. In the experiments on Yale face database and ORL face database, our method achieves constantly superior performance than those competing methods.
Marc G. Berman - One of the best experts on this subject based on the ideXlab platform.
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pattern classification of fmri data applications for analysis of spatially distributed cortical networks
NeuroImage, 2014Co-Authors: Grigori Yourganov, Tanya Schmah, Marc G. Berman, Nathan W Churchill, Cheryl L. Grady, Stephen C StrotherAbstract:Abstract The field of fMRI data analysis is rapidly growing in sophistication, particularly in the domain of multivariate pattern classification. However, the interaction between the properties of the analytical model and the parameters of the BOLD signal (e.g. signal magnitude, temporal variance and functional connectivity) is still an open problem. We addressed this problem by evaluating a set of pattern classification algorithms on simulated and experimental block-design fMRI data. The set of classifiers consisted of linear and quadratic Discriminants, linear support vector machine, and linear and nonlinear Gaussian naive Bayes classifiers. For linear Discriminant, we used two methods of regularization: principal component analysis, and ridge regularization. The classifiers were used (1) to classify the volumes according to the behavioral task that was performed by the subject, and (2) to construct spatial maps that indicated the relative contribution of each voxel to classification. Our evaluation metrics were: (1) accuracy of out-of-sample classification and (2) reproducibility of spatial maps. In simulated data sets, we performed an additional evaluation of spatial maps with ROC analysis. We varied the magnitude, temporal variance and connectivity of simulated fMRI signal and identified the optimal classifier for each simulated environment. Overall, the best performers were linear and quadratic Discriminants (operating on principal components of the data matrix) and, in some rare situations, a nonlinear Gaussian naive Bayes classifier. The results from the simulated data were supported by within-subject analysis of experimental fMRI data, collected in a study of aging. This is the first study that systematically characterizes interactions between analysis model and signal parameters (such as magnitude, variance and correlation) on the performance of pattern classifiers for fMRI.
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Pattern classification of fMRI data: Applications for analysis of spatially distributed cortical networks
NeuroImage, 2014Co-Authors: Grigori Yourganov, Tanya Schmah, Marc G. Berman, Nathan W Churchill, Cheryl L. Grady, Stephen C StrotherAbstract:The field of fMRI data analysis is rapidly growing in sophistication, particularly in the domain of multivariate pattern classification. However, the interaction between the properties of the analytical model and the parameters of the BOLD signal (e.g. signal magnitude, temporal variance and functional connectivity) is still an open problem. We addressed this problem by evaluating a set of pattern classification algorithms on simulated and experimental block-design fMRI data. The set of classifiers consisted of linear and quadratic Discriminants, linear support vector machine, and linear and nonlinear Gaussian naive Bayes classifiers. For linear Discriminant, we used two methods of regularization: principal component analysis, and ridge regularization. The classifiers were used (1) to classify the volumes according to the behavioral task that was performed by the subject, and (2) to construct spatial maps that indicated the relative contribution of each voxel to classification. Our evaluation metrics were: (1) accuracy of out-of-sample classification and (2) reproducibility of spatial maps. In simulated data sets, we performed an additional evaluation of spatial maps with ROC analysis. We varied the magnitude, temporal variance and connectivity of simulated fMRI signal and identified the optimal classifier for each simulated environment. Overall, the best performers were linear and quadratic Discriminants (operating on principal components of the data matrix) and, in some rare situations, a nonlinear Gaussian naïve Bayes classifier. The results from the simulated data were supported by within-subject analysis of experimental fMRI data, collected in a study of aging. This is the first study that systematically characterizes interactions between analysis model and signal parameters (such as magnitude, variance and correlation) on the performance of pattern classifiers for fMRI. © 2014 Elsevier Inc.
