scholarly journals The Smoothing Artifact of Spatially Constrained Canonical Correlation Analysis in Functional MRI

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Dietmar Cordes ◽  
Mingwu Jin ◽  
Tim Curran ◽  
Rajesh Nandy
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Time Course ◽  
Memory Task ◽  
Motor Task ◽  
Test Statistic ◽  
Multivariate Statistical ◽  
Activation Patterns ◽  
Wide Range

A wide range of studies show the capacity of multivariate statistical methods for fMRI to improve mapping of brain activations in a noisy environment. An advanced method uses local canonical correlation analysis (CCA) to encompass a group of neighboring voxels instead of looking at the single voxel time course. The value of a suitable test statistic is used as a measure of activation. It is customary to assign the value to the center voxel; however, this is a choice of convenience and without constraints introduces artifacts, especially in regions of strong localized activation. To compensate for these deficiencies, different spatial constraints in CCA have been introduced to enforce dominance of the center voxel. However, even if the dominance condition for the center voxel is satisfied, constrained CCA can still lead to a smoothing artifact, often called the “bleeding artifact of CCA”, in fMRI activation patterns. In this paper a new method is introduced to measure and correct for the smoothing artifact for constrained CCA methods. It is shown that constrained CCA methods corrected for the smoothing artifact lead to more plausible activation patterns in fMRI as shown using data from a motor task and a memory task.

scholarly journals Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Mingwu Jin ◽  
Rajesh Nandy ◽  
Tim Curran ◽  
Dietmar Cordes
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Fmri Data ◽  
General Linear ◽  
Design Matrix ◽  
Test Statistic ◽  
Major Drawback ◽  
Activation Patterns ◽  
Linear Contrasts

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventionalt-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic.

scholarly journals Canonical Correlation Analysis of Data on Human-Automation Interaction

1997 ◽  
Vol 41 (1) ◽  
pp. 62-65 ◽  
Author(s):  
Michael G. Shafto ◽  
Asaf Degani ◽  
Alex Kirlik
Keyword(s):  
Correlation Analysis ◽  
Human Factors ◽  
Field Study ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Statistical Significance ◽  
Study Data ◽  
Commercial Aviation ◽  
Wide Range ◽  
Mission Simulation

Canonical correlation analysis is a type of multivariate linear statistical analysis, first described by Hotelling (1935), which is used in a wide range of disciplines to analyze the relationships between multiple independent and multiple dependent variables. We argue that canonical correlation analysis is the method of choice for use with many kinds of datasets encountered in human factors research, including field-study data, part-task and full-mission simulation data, and flight-recorder data. Although canonical correlation analysis is documented in standard textbooks and is available in many statistical computing packages, there are some technical and interpretive problems which prevent its routine use by human factors practitioners. These include problems of computation, interpretation, statistical significance, and treatment of discrete variables. In this paper we discuss these problems and suggest solutions to them. We illustrate the problems and their solutions based on our experience in using canonical correlation in the analysis of a field study of crew-automation interaction in commercial aviation.

scholarly journals A discriminative analysis framework for multi-modal information fusion

2021 ◽  
Author(s):  
Lei Gao
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Information Fusion ◽  
Canonical Correlation ◽  
Recognition Performance ◽  
Analysis Framework ◽  
Unified Framework ◽  
Special Cases ◽  
Wide Range ◽  
Discriminative Analysis

Since multi-modal data contain rich information about the semantics presented in the sensory and media data, valid interpretation and integration of multi-modal information is recognized as a central issue for the successful utilization of multimedia in a wide range of applications. Thus, multi-modal information analysis is becoming an increasingly important research topic in the multimedia community. However, the effective integration of multi-modal information is a difficult problem, facing major challenges in the identification and extraction of complementary and discriminatory features, and the impactful fusion of information from multiple channels. In order to address the challenges, in this thesis, we propose a discriminative analysis framework (DAF) for high performance multi-modal information fusion. The proposed framework has two realizations. We first introduce Discriminative Multiple Canonical Correlation Analysis (DMCCA) as the fusion component of the framework. DMCCA is capable of extracting more discriminative characteristics from multi-modal information. We demonstrate that optimal performance by DMCCA can be analytically and graphically verified, and Canonical Correlation Analysis (CCA), Multiple Canonical Correlation Analysis (MCCA) and Discriminative Canonical Correlation Analysis (DCCA) are special cases of DMCCA, thus establishing a unified framework for canonical correlation analysis. To further enhance the performance of discriminative analysis in multi-modal information fusion, Kernel Entropy Component Analysis (KECA) is brought in to analyze the projected vectors in DMCCA space, and thus forming the second realization of the framework. By doing so, not only the discriminative relation is considered in DMCCA space, but also the inherent complementary representation of the input data is revealed by entropy estimation, leading to better utilization of the multi-modal information and better pattern recognition performance. Finally, we implement a prototype of the proposed DAF to demonstrate its performance in handwritten digit recognition, face recognition and human emotion recognition. Extensive experiments show that the proposed framework outperforms the existing methods based on similar principles, clearly demonstrating the generic nature of the framework. Furthermore, this work offers a promising direction to design advanced multi-modal information fusion systems with great potential to impact the development of intelligent human computer interaction systems.

scholarly journals PERBANDINGAN NILAI KORELASI PADA KANONIK ROBUST (METODE MINIMUM COVARIANCE DETERMINANT) DAN KANONIK KLASIK (Studi Kasus Data Struktur Ekonomi dan Kesejahteraan Rakyat di Jawa Barat 2016)

2019 ◽  
Vol 8 (4) ◽  
pp. 530-541
Author(s):  
Widi Rahayu ◽  
Sudarno Sudarno ◽  
Alan Prahutama
Keyword(s):  
Statistical Analysis ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Economic Structure ◽  
Multivariate Approach ◽  
Minimum Covariance Determinant ◽  
Multivariate Statistical ◽  
Final Project ◽  
Test Of Significance

Canonical correlation analysis is a multivariate statistical analysis that aims to examine the correlation between two groups of variabels in a way to maximize the value of correlation between variabels. The outlier in the data affect the covariance matrix is generated, So that use robust multivarat. There is robust multivariate approach to the analysis of canonical robust with MCD method (Minimum Covariance Determinant). This final project aims to determine comparison between correlation value of robust canonical with MCD and canonical classical methods. With a data theres containing of outliers in the case studies of people's welfare and economic structures in West Java in 2016. Used a set of variabels welfare of people consist of 6 variabel (Y) and a set of variabels economic structure which consists of four variabels (X). Based on the analysis results obtained that robust canonical correlation values better explain the correlation between two sets of variabels, the correlation value 0.99552, =0.91228, =0.71529, =0.63174, While the correlation value on classical canonical are 0.931489, 0.538672, 0.387099, 0.259318, Canonical robust can be interpreted more because it meets the test of significance are partially and directly, while the classical canon can not be interpreted further because it does not meet the test of the significance of the function. Keywords       : Classical canonical correlation, canonical correlation robust correlation value, Minimum Covariance Determinant (MCD)

ON THE PROPERTIES OF SOME TESTS FOR COMMON STOCHASTIC TRENDS

2002 ◽  
Vol 18 (6) ◽  
pp. 1336-1349
Author(s):  
Jörg Breitung ◽  
Carsten Trenkler
Keyword(s):  
Monte Carlo ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Asymptotic Properties ◽  
Small Sample ◽  
Test Statistics ◽  
Test Statistic ◽  
Stochastic Trends ◽  
Small Sample Properties

We study the asymptotic properties of the tests suggested by Choi and Ahn (1995, Econometric Theory 11, 952–983) in the case of a (nearly) improper normalization of the cointegration vectors. To overcome the size problems in such situations we suggest a test statistic that is based on the eigenvalues of a canonical correlation analysis. Using Monte Carlo simulations, the small sample properties of our test are compared to various other test statistics recently suggested in the literature.

scholarly journals Orthonormal Canonical Correlation Analysis

2021 ◽  
Vol 2 (1) ◽  
pp. 24-36
Author(s):  
Stan Lipovetsky
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Canonical Correlations ◽  
Robust Version ◽  
Multiple Variables ◽  
Value Decomposition ◽  
Further Development

Abstract Complex managerial problems are usually described by datasets with multiple variables, and in lack of a theoretical model, the data structures can be found by special multivariate statistical techniques. For two datasets, the canonical correlation analysis and its robust version are known as good working research tools. This paper presents their further development via the orthonormal approximation of data matrices which corresponds to using singular value decomposition in the canonical correlations. The features of the new method are described and applications considered. This type of multivariate analysis is useful for solving various practical problems of applied statistics requiring operating with two data sets, and can be helpful in managerial estimations and decision making.

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