Comparing Canonical Correlation Analysis with Partial Least Squares Regression in Estimating Forest Leaf Area Index with Multitemporal Landsat TM Imagery

2012 ◽  
Vol 49 (1) ◽  
pp. 92-116 ◽  
Author(s):  
Ruiliang Pu
Keyword(s):  
Correlation Analysis ◽  
Leaf Area Index ◽  
Least Squares ◽  
Leaf Area ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Partial Least Squares Regression ◽  
Least Squares Regression ◽  
Area Index ◽  
Landsat Tm Imagery

A Neural Network Based on Canonical Correlation for Multicollinearity Diagnosis

2013 ◽  
Vol 756-759 ◽  
pp. 3324-3329
Author(s):  
Ji Fu Nong
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Partial Least Squares Regression ◽  
Real Data ◽  
Data Sets ◽  
Parameter Setting ◽  
Least Squares Regression ◽  
Penalty Term ◽  
General Data

We review a recent neural implementation of Canonical Correlation Analysis and show, using ideas suggested by Ridge Regression, how to make the algorithm robust. The network is shown to operate on data sets which exhibit multicollinearity. We develop a second model which not only performs as well on multicollinear data but also on general data sets. This model allows us to vary a single parameter so that the network is capable of performing Partial Least Squares regression to Canonical Correlation Analysis and every intermediate operation between the two. On multicollinear data, the parameter setting is shown to be important but on more general data no particular parameter setting is required. Finally, we develop a second penalty term which acts on such data as a smoother in that the resulting weight vectors are much smoother and more interpretable than the weights without the robustification term. We illustrate our algorithms on both artificial and real data.

Creativity as a Tool of Tourism Development

2020 ◽  
pp. 504-530
Author(s):  
Alžbeta Kiráľová
Keyword(s):  
Czech Republic ◽  
Correlation Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Tourism Development ◽  
Multivariate Methods ◽  
The Czech Republic ◽  
Future Studies

This chapter shows how creativity is bounded with tourism development in the destination. It points out the influence of changes in visitors´ behavior on the destinations, defines creativity, and discusses the relation of culture and creativity in tourism. The chapter focuses on the relation between creativity and development of tourism in the Czech Republic´s regions in the pre-crisis, crisis and after-crisis period. The destinations were subjects to research using two multivariate methods i.e. canonical correlation analysis (CCA) and partial least squares (PLS). The chapter also makes suggestions for future studies.

Other Tensor Analysis and Further Direction

2011 ◽  
pp. 226-252
Author(s):  
David Zhang ◽  
Fengxi Song ◽  
Yong Xu ◽  
Zhizhen Liang
Keyword(s):  
Correlation Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Tensor Analysis ◽  
Tensor Methods

In this chapter, we describe tensor-based classifiers, tensor canonical correlation analysis and tensor partial least squares, which can be used in biometrics. Section 11.1 gives background and devolvement of these tensor methods. Section 11.2 introduces tensor-based classifiers. Section 11.3 gives tensor canonical correlation analysis and tensor partial least squares. We summarize this chapter in Section 11.4.

Constrained ERM Learning of Canonical Correlation Analysis: A Least Squares Perspective

2017 ◽  
Vol 29 (10) ◽  
pp. 2825-2859 ◽  
Author(s):  
Jia Cai ◽  
Hongwei Sun
Keyword(s):  
Correlation Analysis ◽  
Least Squares ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Data Sets ◽  
Risk Minimization ◽  
Real World Data ◽  
Empirical Risk ◽  
Statistical Consistency ◽  
Kaczmarz Method

Canonical correlation analysis (CCA) is a useful tool in detecting the latent relationship between two sets of multivariate variables. In theoretical analysis of CCA, a regularization technique is utilized to investigate the consistency of its analysis. This letter addresses the consistency property of CCA from a least squares view. We construct a constrained empirical risk minimization framework of CCA and apply a two-stage randomized Kaczmarz method to solve it. In the first stage, we remove the noise, and in the second stage, we compute the canonical weight vectors. Rigorous theoretical consistency is addressed. The statistical consistency of this novel scenario is extended to the kernel version of it. Moreover, experiments on both synthetic and real-world data sets demonstrate the effectiveness and efficiency of the proposed algorithms.

Creativity as a Tool of Tourism Development

2017 ◽  
pp. 67-93
Author(s):  
Alžbeta Kiráľová
Keyword(s):  
Czech Republic ◽  
Correlation Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Tourism Development ◽  
Multivariate Methods ◽  
The Czech Republic ◽  
Future Studies

This chapter shows how creativity is bounded with tourism development in the destination. It points out the influence of changes in visitors´ behavior on the destinations, defines creativity, and discusses the relation of culture and creativity in tourism. The chapter focuses on the relation between creativity and development of tourism in the Czech Republic´s regions in the pre-crisis, crisis and after-crisis period. The destinations were subjects to research using two multivariate methods i.e. canonical correlation analysis (CCA) and partial least squares (PLS). The chapter also makes suggestions for future studies.

scholarly journals Corticomuscular Activity Modeling by Combining Partial Least Squares and Canonical Correlation Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Xun Chen ◽  
Aiping Liu ◽  
Z. Jane Wang ◽  
Hu Peng
Keyword(s):  
Correlation Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Medical Knowledge ◽  
Data Sets ◽  
Data Set ◽  
Activity Modeling ◽  
Multiple Data Sets

Corticomuscular activity modeling based on multiple data sets such as electroencephalography (EEG) and electromyography (EMG) signals provides a useful tool for understanding human motor control systems. In this paper, we propose modeling corticomuscular activity by combining partial least squares (PLS) and canonical correlation analysis (CCA). The proposed method takes advantage of both PLS and CCA to ensure that the extracted components are maximally correlated across two data sets and meanwhile can well explain the information within each data set. This complementary combination generalizes the statistical assumptions beyond both PLS and CCA methods. Simulations were performed to illustrate the performance of the proposed method. We also applied the proposed method to concurrent EEG and EMG data collected in a Parkinson’s disease (PD) study. The results reveal several highly correlated temporal patterns between EEG and EMG signals and indicate meaningful corresponding spatial activation patterns. In PD subjects, enhanced connections between occipital region and other regions are noted, which is consistent with previous medical knowledge. The proposed framework is a promising technique for performing multisubject and bimodal data analysis.

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