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Stephen C Strother – One of the best experts on this subject based on the ideXlab platform.

  • pattern classification of fmri data applications for analysis of spatially distributed cortical networks
    NeuroImage, 2014
    Co-Authors: Grigori Yourganov, Tanya Schmah, Nathan W Churchill, Marc G. Berman, Cheryl L. Grady, Stephen C Strother
    Abstract:

    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.

  • Pattern classification of fMRI data: Applications for analysis of spatially distributed cortical networks
    NeuroImage, 2014
    Co-Authors: Grigori Yourganov, Tanya Schmah, Nathan W Churchill, Marc G. Berman, Cheryl L. Grady, Stephen C Strother
    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 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.

  • from classifiers to discriminators a nearest neighbor rule induced Discriminant analysis
    Pattern Recognition, 2011
    Co-Authors: Jian Yang, Jingyu Yang, Lei Zhang, David Zhang
    Abstract:

    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.

  • Block-based two-dimensional scatter-difference Discriminant analysis and face recognition
    2008 7th World Congress on Intelligent Control and Automation, 2008
    Co-Authors: Caikou Chen, Jingyu Yang
    Abstract:

    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.

  • 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, 2008
    Co-Authors: Sheng Li, David Zhang, Zhu-li Shao, Xiao-yuan Jing, Jingyu Yang
    Abstract:

    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.

Grigori Yourganov – One of the best experts on this subject based on the ideXlab platform.

  • pattern classification of fmri data applications for analysis of spatially distributed cortical networks
    NeuroImage, 2014
    Co-Authors: Grigori Yourganov, Tanya Schmah, Nathan W Churchill, Marc G. Berman, Cheryl L. Grady, Stephen C Strother
    Abstract:

    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.

  • Pattern classification of fMRI data: Applications for analysis of spatially distributed cortical networks
    NeuroImage, 2014
    Co-Authors: Grigori Yourganov, Tanya Schmah, Nathan W Churchill, Marc G. Berman, Cheryl L. Grady, Stephen C Strother
    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 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.