scholarly journals Robust nonlinear canonical correlation analysis: application to seasonal climate forecasting

2008 ◽  
Vol 15 (1) ◽  
pp. 221-232 ◽  
Author(s):  
A. J. Cannon ◽  
W. W. Hsieh
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Mean Squared Error ◽  
Synthetic Dataset ◽  
Model Parameters ◽  
Inverse Mapping ◽  
Seasonal Climate ◽  
The Mean ◽  
Improved Performance

Abstract. Robust variants of nonlinear canonical correlation analysis (NLCCA) are introduced to improve performance on datasets with low signal-to-noise ratios, for example those encountered when making seasonal climate forecasts. The neural network model architecture of standard NLCCA is kept intact, but the cost functions used to set the model parameters are replaced with more robust variants. The Pearson product-moment correlation in the double-barreled network is replaced by the biweight midcorrelation, and the mean squared error (mse) in the inverse mapping networks can be replaced by the mean absolute error (mae). Robust variants of NLCCA are demonstrated on a synthetic dataset and are used to forecast sea surface temperatures in the tropical Pacific Ocean based on the sea level pressure field. Results suggest that adoption of the biweight midcorrelation can lead to improved performance, especially when a strong, common event exists in both predictor/predictand datasets. Replacing the mse by the mae leads to improved performance on the synthetic dataset, but not on the climate dataset except at the longest lead time, which suggests that the appropriate cost function for the inverse mapping networks is more problem dependent.

Achievement-Related Motives and Work-Related Attitudes of Japanese and Chinese Students

1993 ◽  
Vol 73 (3_part_1) ◽  
pp. 755-767
Author(s):  
Hirotsugu Yamauchi ◽  
Yan Li
Keyword(s):  
Correlation Analysis ◽  
Achievement Motivation ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Chinese Students ◽  
Japanese Students ◽  
Work Related ◽  
Students First ◽  
The Mean ◽  
The Relationship

This study was designed to provide data for comparisons of achievement-related motives and work-related attitudes for Japanese and Chinese students. First, the mean scores on achievement-related motives and work-related attitudes of Japanese students (124 men and 174 women) were compared with those of Chinese students (222 men and 130 women). Chinese students displayed stronger motives and attitudes toward successful achievement. Second, the factorial structures of achievement-related motives for each sample were compared. There were some similarities between samples except one factor based on the motive to avoid failure showed an opposite feature. Third, the relationship between achievement-related motives and work-related attitudes was examined for each sample. A canonical correlation analysis provided two variates, instrumental activity arises from achievement motivation in a work-related situation and hope of success in their competitive lives includes economic success.

scholarly journals Incremental Canonical Correlation Analysis

2020 ◽  
Vol 10 (21) ◽  
pp. 7827
Author(s):  
Hongmin Zhao ◽  
Dongting Sun ◽  
Zhigang Luo
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Large Scale ◽  
Feature Learning ◽  
Memory Storage ◽  
Matrix Inverse ◽  
Large Memory ◽  
The Mean ◽  
Value Decomposition

Canonical correlation analysis (CCA) is a kind of a simple yet effective multiview feature learning technique. In general, it learns separate subspaces for two views by maximizing their correlations. However, there still exist two restrictions to limit its applicability for large-scale datasets, such as videos: (1) sufficiently large memory requirements and (2) high-computation complexity for matrix inverse. To address these issues, we propose an incremental canonical correlation analysis (ICCA), which maintains in an adaptive manner a constant memory storage for both the mean and covariance matrices. More importantly, to avoid matrix inverse, we save overhead time by using sequential singular value decomposition (SVD), which is still efficient in case when the number of samples is sufficiently few. Driven by visual tracking, which tracks a specific target in a video sequence, we readily apply the proposed ICCA for this task through some essential modifications to evaluate its efficacy. Extensive experiments on several video sequences show the superiority of ICCA when compared to several classical trackers.

scholarly journals Model Tuning with Canonical Correlation Analysis

2014 ◽  
Vol 142 (5) ◽  
pp. 2018-2027 ◽  
Author(s):  
Caren Marzban ◽  
Scott Sandgathe ◽  
James D. Doyle
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Convective Precipitation ◽  
Model Parameters ◽  
Prediction System ◽  
Multivariate Statistical ◽  
The Difference ◽  
Model Tuning ◽  
The Relationship

Abstract Knowledge of the relationship between model parameters and forecast quantities is useful because it can aid in setting the values of the former for the purpose of having a desired effect on the latter. Here it is proposed that a well-established multivariate statistical method known as canonical correlation analysis can be formulated to gauge the strength of that relationship. The method is applied to several model parameters in the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) for the purpose of “controlling” three forecast quantities: 1) convective precipitation, 2) stable precipitation, and 3) snow. It is shown that the model parameters employed here can be set to affect the sum, and the difference between convective and stable precipitation, while keeping snow mostly constant; a different combination of model parameters is shown to mostly affect the difference between stable precipitation and snow, with minimal effect on convective precipitation. In short, the proposed method cannot only capture the complex relationship between model parameters and forecast quantities, it can also be utilized to optimally control certain combinations of the latter.

Analysis of Maximum Expiratory Flow Volume Curves Using Canonical Correlation Analysis

1985 ◽  
Vol 24 (02) ◽  
pp. 91-100 ◽  
Author(s):  
W. van Pelt ◽  
Ph. H. Quanjer ◽  
M. E. Wise ◽  
E. van der Burg ◽  
R. van der Lende
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Flow Volume ◽  
Non Linear ◽  
Maximum Expiratory Flow ◽  
Expiratory Flow ◽  
Relationship Of ◽  
The Relationship ◽  
Age And Sex

SummaryAs part of a population study on chronic lung disease in the Netherlands, an investigation is made of the relationship of both age and sex with indices describing the maximum expiratory flow-volume (MEFV) curve. To determine the relationship, non-linear canonical correlation was used as realized in the computer program CANALS, a combination of ordinary canonical correlation analysis (CCA) and non-linear transformations of the variables. This method enhances the generality of the relationship to be found and has the advantage of showing the relative importance of categories or ranges within a variable with respect to that relationship. The above is exemplified by describing the relationship of age and sex with variables concerning respiratory symptoms and smoking habits. The analysis of age and sex with MEFV curve indices shows that non-linear canonical correlation analysis is an efficient tool in analysing size and shape of the MEFV curve and can be used to derive parameters concerning the whole curve.

Sign in / Sign up

Export Citation Format

Share Document