scholarly journals Representation-Constrained Canonical Correlation Analysis: A Hybridization of Canonical Correlation and Principal Component Analyses

2009 ◽  
Author(s):  
S. K. Mishra
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Principal Component ◽  
Principal Component Analyses

scholarly journals Analysis of Soil-Vegetation Interrelationships in a South-Southern Secondary Forest of Nigeria

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
D. D. Eni ◽  
A. I. Iwara ◽  
R. A. Offiong
Keyword(s):  
Organic Matter ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
State Government ◽  
Canonical Correlation ◽  
Secondary Forest ◽  
Principal Component ◽  
Tree Density ◽  
Tree Size ◽  
Canonical Variate

Soil-vegetation interrelationships in a secondary forest of South-Southern Nigeria were studied using principal component analysis (PCA) and canonical correlation analysis (CCA). The grid system of vegetation sampling was employed to randomly collect vegetation and soil data from fifteen quadrats of 10 m × 10 m. PCA result showed that exchangeable sodium, organic matter, cation exchange capacity, exchangeable calcium, and sand content were the major soil properties sustaining the regenerative capacity and luxuriant characteristics of the secondary forest, while tree size and tree density constituted the main vegetation parameters protecting and enriching the soil for its continuous support to the vegetation after decades of anthropogenic disturbance (food crop cultivation and illegal logging activities) before its acquisition and subsequent preservation by the Cross River State government in 2003. In addition, canonical correlation analysis showed result similar to PCA, as it indicated a pattern of relationship between soil and vegetation. The only retained canonical variate revealed a positive interrelationship between organic matter and tree size as well as an inverse relationship between organic matter and tree density. These extracted soil and vegetation variables are indeed significantly important in explaining soil-vegetation interrelationships in the highly regenerative secondary forest.

scholarly journals Multiway generalized canonical correlation analysis

Biostatistics ◽  
2020 ◽  
Author(s):  
Arnaud Gloaguen ◽  
Cathy Philippe ◽  
Vincent Frouin ◽  
Giulia Gennari ◽  
Ghislaine Dehaene-Lambertz ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Principal Component ◽  
Least Squares Regression ◽  
Analysis Framework ◽  
Convergence Properties ◽  
Human Infants ◽  
Generalized Canonical Correlation Analysis ◽  
Cognitive Study

Summary Regularized generalized canonical correlation analysis (RGCCA) is a general multiblock data analysis framework that encompasses several important multivariate analysis methods such as principal component analysis, partial least squares regression, and several versions of generalized canonical correlation analysis. In this article, we extend RGCCA to the case where at least one block has a tensor structure. This method is called multiway generalized canonical correlation analysis (MGCCA). Convergence properties of the MGCCA algorithm are studied, and computation of higher-level components are discussed. The usefulness of MGCCA is shown on simulation and on the analysis of a cognitive study in human infants using electroencephalography (EEG).

scholarly journals A PCA–CCA network for RGB-D object recognition

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141775282 ◽  
Author(s):  
Shiying Sun ◽  
Ning An ◽  
Xiaoguang Zhao ◽  
Min Tan
Keyword(s):  
Principal Component Analysis ◽  
Object Recognition ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Principal Component ◽  
Component Analysis ◽  
Depth Information ◽  
Processing Unit ◽  
Data Set

Object recognition is one of the essential issues in computer vision and robotics. Recently, deep learning methods have achieved excellent performance in red-green-blue (RGB) object recognition. However, the introduction of depth information presents a new challenge: How can we exploit this RGB-D data to characterize an object more adequately? In this article, we propose a principal component analysis–canonical correlation analysis network for RGB-D object recognition. In this new method, two stages of cascaded filter layers are constructed and followed by binary hashing and block histograms. In the first layer, the network separately learns principal component analysis filters for RGB and depth. Then, in the second layer, canonical correlation analysis filters are learned jointly using the two modalities. In this way, the different characteristics of the RGB and depth modalities are considered by our network as well as the characteristics of the correlation between the two modalities. Experimental results on the most widely used RGB-D object data set show that the proposed method achieves an accuracy which is comparable to state-of-the-art methods. Moreover, our method has a simpler structure and is efficient even without graphics processing unit acceleration.

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