Improved Teleconnective Predictability of Monthly Precipitation Amounts Using Canonical Correlation Analysis

2021 ◽  
Author(s):  
Mahdi Ghamghami ◽  
Javad Bazrafshan
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Lead Time ◽  
Large Scale ◽  
Climate Indices ◽  
Lead Times ◽  
Monthly Precipitation ◽  
Weather Stations ◽  
Monthly Precipitation Data

Abstract This study aimed to evaluate the application of the canonical correlation analysis (CCA) to predict monthly precipitation amounts (predictands) by benefitting from 17 large-scale climate indices (predictors) in Iran. Monthly precipitation data, covering the period of 1987–2017, were collected from 100 weather stations across the country. Monthly precipitations were predicted using the multiple linear regression (MLR) models, based on the 1- to 6-month lead times of the original and canonical predictors. The cross-validation was conducted to compare the prediction skills of the two sets of MLR models constructed on the basis of the original predictors (MLOrigPr) and the canonical predictors (MLCCAPr). The analyses revealed the dominant teleconnections and that there are the interannual variations in responses of precipitation to them suggesting that a signal only is not sufficient to achieve a robust understanding of the associations. At the 1-month lead time, the MLR models based on the canonical predictors outperformed those based on the original predictors. However, the skill of both models was reduced by increasing the lead times up to 6 months. Averaging on all stations, around 61.4% and 26.3% of the observed values falls into the 95% prediction intervals of the MLCCAPr and MLOrigPr models, respectively. Furthermore, the MLCCAPr models were found to be more spatially universal than the MLOrigPr ones. These findings corroborated the advantage of using the CCA in improving the teleconnective predictability of precipitation in Iran.

scholarly journals Structured SUMCOR Multiview Canonical Correlation Analysis for Large-Scale Data

2019 ◽  
Vol 67 (2) ◽  
pp. 306-319 ◽  
Author(s):  
Charilaos I. Kanatsoulis ◽  
Xiao Fu ◽  
Nicholas D. Sidiropoulos ◽  
Mingyi Hong
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Large Scale ◽  
Large Scale Data ◽  
Scale Data

scholarly journals A Jacobi–Davidson Method for Large Scale Canonical Correlation Analysis

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 229
Author(s):  
Zhongming Teng ◽  
Xiaowei Zhang
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Large Scale ◽  
Numerical Examples ◽  
Canonical Correlations ◽  
Generalized Eigenvalue ◽  
Convergence Results ◽  
Davidson Method ◽  
Canonical Weight

In the large scale canonical correlation analysis arising from multi-view learning applications, one needs to compute canonical weight vectors corresponding to a few of largest canonical correlations. For such a task, we propose a Jacobi–Davidson type algorithm to calculate canonical weight vectors by transforming it into the so-called canonical correlation generalized eigenvalue problem. Convergence results are established and reveal the accuracy of the approximate canonical weight vectors. Numerical examples are presented to support the effectiveness of the proposed method.

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 Process Monitoring Using Canonical correlation analysis

10.31645/2021 ◽  
2019 ◽  
Author(s):  
Muahmmad Shakir ◽  
Keyword(s):  
Correlation Analysis ◽  
Process Monitoring ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Large Scale ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Process Data ◽  
Industrial Plants

Principal component analysis (PCA) and partial least square (PLS) used for fault diagnosis and process monitoring for systems. It is assumed that the data to be investigated is not self-correlated. However, the most large-scale chemical industrial plants are nonlinear in nature so these techniques do not cope with them, invalid in nature. To fulfil the gap, there is need to develop an algorithm which can manage these nonlinearities of the process. The demands of industrial products are increasing rapidly so different adaptable techniques are being proposed. Canonical Correlation Analysis (CCA) is multivariate data-driven methodology that takes input-output both process data into consideration. Most industrial systems assumed that the data to be analysed is Gaussian in nature. However, it is not due to the non-linearity’sreal systems in nature. In this work, an algorithm is developed that can monitor the system process using CCA with control limit that is achieved from the kernel density estimation by estimating probability density function (pdf).

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.

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