Canonical Correlation Analysis for Market and Environmental Factors of Vegetable using Functional Data Methods

2021 ◽  
Vol 23 (3) ◽  
pp. 1469-1481
Author(s):  
Soyou Park ◽  
Sanghun Jeong ◽  
Hojin Yang
Keyword(s):  
Environmental Factors ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Functional Data ◽  
Canonical Correlation

scholarly journals Kernel functional canonical correlation analysis

2017 ◽  
Vol 5 (325) ◽  
Author(s):  
Mirosław Krzyśko ◽  
Łukasz Waszak
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Functional Data ◽  
Canonical Correlation ◽  
Multivariate Data ◽  
Research Interest ◽  
Canonical Correlations ◽  
Canonical Variables ◽  
Correlation Methods ◽  
The Subject

Canonical correlation methods for data representing functions or curves have received much attention in recent years. Such data, known in the literature as functional data (Ramsay and Silverman, 2005), has been the subject of much recent research interest. Examples of functional data can be found in several application domains, such as medicine, economics, meteorology and many others. Unfortunately, the multivariate data canonical correlation methods cannot be used directly for functional data, because of the problem of dimensionality and difficulty in taking into account the correlation and order of functional data. The problem of constructing canonical correlations and canonical variables for functional data was addressed by Leurgans et al. (1993), and further developments were made by Ramsay and Silverman (2005). In this paper we propose a new method of constructing canonical correlations and canonical variables for functional data.

scholarly journals Generalized canonical correlation analysis for functional data

2020 ◽  
Vol 57 (1) ◽  
pp. 1-12
Author(s):  
Tomasz Górecki ◽  
Mirosław Krzyśko ◽  
Waldemar Wołyński
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Functional Data ◽  
Canonical Correlation ◽  
Joint Analysis ◽  
Data Sets ◽  
Complex Data ◽  
Generalized Canonical Correlation Analysis ◽  
Multiple Variable ◽  
Multivariate Functional Data

SummaryThere is a growing need to analyze data sets characterized by several sets of variables observed on the same set of individuals. Such complex data structures are known as multiblock (or multiple-set) data sets. Multi-block data sets are encountered in diverse fields including bioinformatics, chemometrics, food analysis, etc. Generalized Canonical Correlation Analysis (GCCA) is a very powerful method to study this kind of relationships between blocks. It can also be viewed as a method for the integration of information from K > 2 distinct sources (Takane and Oshima-Takane 2002). In this paper, GCCA is considered in the context of multivariate functional data. Such data are treated as realizations of multivariate random processes. GCCA is a technique that allows the joint analysis of several sets of data through dimensionality reduction. The central problem of GCCA is to construct a series of components aiming to maximize the association among the multiple variable sets. This method will be presented for multivariate functional data. Finally, a practical example will be discussed.

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