The Experts below are selected from a list of 1315074 Experts worldwide ranked by ideXlab platform
Michael A Saunders - One of the best experts on this subject based on the ideXlab platform.
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sparse canonical Correlation Analysis
arXiv: Machine Learning, 2017Co-Authors: Victor Minden, Bradley J. Nelson, Michael A SaundersAbstract:Canonical Correlation Analysis was proposed by Hotelling [6] and it measures linear relationship between two multidimensional variables. In high dimensional setting, the classical canonical Correlation Analysis breaks down. We propose a sparse canonical Correlation Analysis by adding l1 constraints on the canonical vectors and show how to solve it efficiently using linearized alternating direction method of multipliers (ADMM) and using TFOCS as a black box. We illustrate this idea on simulated data.
Victor Minden - One of the best experts on this subject based on the ideXlab platform.
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sparse canonical Correlation Analysis
arXiv: Machine Learning, 2017Co-Authors: Victor Minden, Bradley J. Nelson, Michael A SaundersAbstract:Canonical Correlation Analysis was proposed by Hotelling [6] and it measures linear relationship between two multidimensional variables. In high dimensional setting, the classical canonical Correlation Analysis breaks down. We propose a sparse canonical Correlation Analysis by adding l1 constraints on the canonical vectors and show how to solve it efficiently using linearized alternating direction method of multipliers (ADMM) and using TFOCS as a black box. We illustrate this idea on simulated data.
Yoshio Takane - One of the best experts on this subject based on the ideXlab platform.
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Functional Multiple-Set Canonical Correlation Analysis
Psychometrika, 2011Co-Authors: Heungsun Hwang, Kwanghee Jung, Yoshio Takane, Todd S WoodwardAbstract:We propose functional multiple-set canonical Correlation Analysis for exploring associations among multiple sets of functions. The proposed method includes functional canonical Correlation Analysis as a special case when only two sets of functions are considered. As in classical multiple-set canonical Correlation Analysis, computationally, the method solves a matrix eigen-Analysis problem through the adoption of a basis expansion approach to approximating data and weight functions. We apply the proposed method to functional magnetic resonance imaging (fMRI) data to identify networks of neural activity that are commonly activated across subjects while carrying out a working memory task.
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Generalized canonical Correlation Analysis with missing values
Computational Statistics, 2011Co-Authors: Michel Van De Velden, Yoshio TakaneAbstract:Generalized canonical Correlation Analysis is a versatile technique that allows the joint Analysis of several sets of data matrices. The generalized canonical Correlation Analysis solution can be obtained through an eigenequation and distributional assumptions are not required. When dealing with multiple set data, the situation frequently occurs that some values are missing. In this paper, two new methods for dealing with missing values in generalized canonical Correlation Analysis are introduced. The first approach, which does not require iterations, is a generalization of the Test Equating method available for principal component Analysis. In the second approach, missing values are imputed in such a way that the generalized canonical Correlation Analysis objective function does not increase in subsequent steps. Convergence is achieved when the value of the objective function remains constant. By means of a simulation study, we assess the performance of the new methods. We compare the results with those of two available methods; the missing-data passive method, introduced in Gifi’s homogeneity Analysis framework, and the GENCOM algorithm developed by Green and Carroll. An application using world bank data is used to illustrate the proposed methods.
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Generalized canonical Correlation Analysis with missing values
2009Co-Authors: Michel Van De Velden, Yoshio TakaneAbstract:Two new methods for dealing with missing values in generalized canonical Correlation Analysis are introduced. The first approach, which does not require iterations, is a generalization of the Test Equating method available for principal component Analysis. In the second approach, missing values are imputed in such a way that the generalized canonical Correlation Analysis objective function does not increase in subsequent steps. Convergence is achieved when the value of the objective function remains constant. By means of a simulation study, we assess the performance of the new methods. We compare the results with those of two available methods; the missing-data passive method, introduced Gifi's homogeneity Analysis framework, and the GENCOM algorithm developed by Green and Carroll.
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Generalized constrained canonical Correlation Analysis
Multivariate Behavioral Research, 2002Co-Authors: Yoshio Takane, Heungsun HwangAbstract:A method for generalized constrained canonical Correlation Analysis (GCCANO) is proposed that incorporates external information on both rows and columns of data matrices. In this method each set of variables is first decomposed into the sum of several submatrices according to the external information, and then canonical Correlation Analysis is applied to pairs of derived submatrices, one from each set, to explore linear relationships between them. Technically, the former amounts to projections of the data matrix onto the spaces spanned by matrices of external information, while the latter involves the generalized singular value decomposition of a matrix with certain metric matrices. GCCANO subsumes a number of existing methods as special cases. It generalizes various kinds of linearly constrained correspondence Analysis as well as multivariate Analysis of variance/canonical discriminant Analysis. Permutation tests are applied to test the significance of canonical Correlations obtained from GCCANO. Example...
John Shawetaylor - One of the best experts on this subject based on the ideXlab platform.
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convergence Analysis of kernel canonical Correlation Analysis theory and practice
Machine Learning, 2009Co-Authors: David Roi Hardoon, John ShawetaylorAbstract:Canonical Correlation Analysis is a technique for finding pairs of basis vectors that maximise the Correlation of a set of paired variables, these pairs can be considered as two views of the same object. This paper provides a convergence Analysis of Canonical Correlation Analysis by defining a pattern function that captures the degree to which the features from the two views are similar. We analyse the convergence using Rademacher complexity, hence deriving the error bound for new data. The Analysis provides further justification for the regularisation of kernel Canonical Correlation Analysis and is corroborated by experiments on real world data.
Bradley J. Nelson - One of the best experts on this subject based on the ideXlab platform.
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sparse canonical Correlation Analysis
arXiv: Machine Learning, 2017Co-Authors: Victor Minden, Bradley J. Nelson, Michael A SaundersAbstract:Canonical Correlation Analysis was proposed by Hotelling [6] and it measures linear relationship between two multidimensional variables. In high dimensional setting, the classical canonical Correlation Analysis breaks down. We propose a sparse canonical Correlation Analysis by adding l1 constraints on the canonical vectors and show how to solve it efficiently using linearized alternating direction method of multipliers (ADMM) and using TFOCS as a black box. We illustrate this idea on simulated data.
