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.

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.

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.

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.

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