Effect of Additional Variables in Principal Component Analysis, Discriminant Analysis and Canonical Correlation Analysis.

1985 ◽  
Author(s):  
Y. Fujikoshi ◽  
P. R. Krishnaiah ◽  
J. Schmidhammer
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Principal Component ◽  
Component Analysis

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.

Statistical Weather Forecasting

1976 ◽  
Vol 98 (1) ◽  
pp. 49-55
Author(s):  
A. Grosjean ◽  
J. L. Kueny
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Correlation Analysis ◽  
Weather Forecasting ◽  
Selection Procedure ◽  
Principal Component ◽  
Component Analysis ◽  
Multiple Correlation ◽  
Multiple Correlation Analysis ◽  
Prediction Problems

This paper illustrates the use of statistical methods such as multiple correlation analysis, discriminant analysis and principal component analysis for weather forecasting. Two examples are presented: the first is a qualitative local prediction of daily precipitation for the next day, using pressure measures of the present day and of past days, by means of discriminant analysis; the second is an analysis of the 500-mbar geopotential heights over Western Europe by means of principal component analysis, followed by a quantitative synoptic prediction of the evolution of these geopotential heights for the next week to come, by means of multiple correlation analysis. For each of these two prediction problems, good predictors are chosen among a great number of candidate ones by a special stepwise selection procedure.

scholarly journals An Alternating Manifold Proximal Gradient Method for Sparse Principal Component Analysis and Sparse Canonical Correlation Analysis

2020 ◽  
Vol 2 (3) ◽  
pp. 192-208
Author(s):  
Shixiang Chen ◽  
Shiqian Ma ◽  
Lingzhou Xue ◽  
Hui Zou
Keyword(s):  
Principal Component Analysis ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Gradient Method ◽  
Canonical Correlation ◽  
Principal Component ◽  
High Dimensional ◽  
Sparse Principal Component Analysis ◽  
Proximal Gradient Method ◽  
Sparse Canonical Correlation Analysis

Sparse principal component analysis and sparse canonical correlation analysis are two essential techniques from high-dimensional statistics and machine learning for analyzing large-scale data. Both problems can be formulated as an optimization problem with nonsmooth objective and nonconvex constraints. Because nonsmoothness and nonconvexity bring numerical difficulties, most algorithms suggested in the literature either solve some relaxations of them or are heuristic and lack convergence guarantees. In this paper, we propose a new alternating manifold proximal gradient method to solve these two high-dimensional problems and provide a unified convergence analysis. Numerical experimental results are reported to demonstrate the advantages of our algorithm.

Succession des communautés de gastéropodes dans deux milieux différant par leur degré d'eutrophisation

1984 ◽  
Vol 62 (11) ◽  
pp. 2317-2327 ◽  
Author(s):  
P. Legendre ◽  
D. Planas ◽  
M.-J. Auclair
Keyword(s):  
Principal Component Analysis ◽  
Correlation Analysis ◽  
Canonical Correlation Analysis ◽  
Canonical Correlation ◽  
Plant Cover ◽  
Principal Component ◽  
The Other ◽  
Nutrient Concentrations ◽  
Benthic Species ◽  
Gastropod Species

This paper compares the succession of gastropods in two environments that are adjacent in space but differ as to their eutrophic level. One is hypereutrophic (du Sud River), the other is mesotrophic (Richelieu River). Canonical correlation analysis brings out the main differences between these two stations, while principal component analysis is used to describe the succession of species within each community. These analyses indicate that the occurrence of gastropod species, as well as their development cycles, may be adapted to the particular synecological evolution of each environment. Thus, the species would not react directly to nutrient concentrations but indirectly, through the effects of these concentrations on oxygen content, plant cover, and predators. In these two environments, some benthic species seem to be good indicators of the eutrophic level of the ecosystem.

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