Principal component analysis of substituent constants

1990 ◽  
Vol 55 (1) ◽  
pp. 55-62 ◽  
Author(s):  
Drahomír Hnyk
Keyword(s):  
Principal Component Analysis ◽  
Inductive Effect ◽  
Resonance Effect ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variance ◽  
Data Matrix ◽  
Cumulative Proportion ◽  
Two Factors ◽  
Substituent Constants

The principal component analysis has been applied to a data matrix formed by 7 usual substituent constants for 38 substituents. Three factors are able to explain 99.4% cumulative proportion of total variance. Several rotations have been carried out for the first two factors in order to obtain their physical meaning. The first factor is related to the resonance effect, whereas the second one expresses the inductive effect, and both together describe 97.5% cumulative proportion of total variance. Their mutual orthogonality does not directly follow from the rotations carried out. With the help of these factors the substituents are divided into four main classes, and some of them assume a special position.

scholarly journals Rice Odours’ Readings Investigation Using Principal Component Analysis

2018 ◽  
Vol 7 (2.29) ◽  
pp. 488
Author(s):  
Nurul Aini Abdul Wahab ◽  
Shamshuritawati Sharif
Keyword(s):  
Principal Component Analysis ◽  
High Dimension ◽  
Electronic Nose ◽  
Principal Component ◽  
Component Analysis ◽  
The Other ◽  
Total Variance ◽  
Scree Plot ◽  
Cumulative Proportion ◽  
Principle Component

The use of electronic nose (e-nose) devices plus principal component analysis can help the process of categorizing the 16 different rice into its type. Generally, the physical feature of an e-nose own more than one hole to capture the odour of rice. For example, the portable e-nose so-called Insniff does have 10 holes (or variables). In this situations, we will have a dataset that consist high-dimension dataset where lead to the presence of interdependencies between all variables under study. Therefore, this study is presented to investigate the odour of rice for identifying the most important variables contributing to the rice odour readings. The principal component analysis (PCA) is implemented to determine the component that best represent the all 10 variables in order to eliminate the interdependency problem, and (2) to identify which variable is considered as important and influential to the newly-formed principle component (PC). The results from PCA suggested that the first two principle components is chosen. It is based on three assessments which are Kaiser’s criterion larger than 1, cumulative proportion of total variance, and scree plot. These two principle components explained 89% of total variance. Results showed that sensor 1 (0.931) and sensor 2 (0.966) are the two important variables that highly contribute to PC1. On the other hand, for PC2, the highest contribution is from sensor 8 (0.828). This study demonstrate that PCA is effective for investigating rice odour readings.  

scholarly journals SVD-based principal component analysis of geochemical data

2005 ◽  
Vol 3 (4) ◽  
pp. 731-741 ◽  
Author(s):  
Petr Praus
Keyword(s):  
Principal Component Analysis ◽  
Euclidean Distance ◽  
Principal Component ◽  
Component Analysis ◽  
Data Matrix ◽  
Geochemical Data ◽  
Average Group ◽  
Testing Data ◽  
Value Decomposition ◽  
Geochemical Variables

AbstractPrincipal Component Analysis (PCA) was used for the mapping of geochemical data. A testing data matrix was prepared from the chemical and physical analyses of the coals altered by thermal and oxidation effects. PCA based on Singular Value Decomposition (SVD) of the standardized (centered and scaled by the standard deviation) data matrix revealed three principal components explaining 85.2% of the variance. Combining the scatter and components weights plots with knowledge of the composition of tested samples, the coal samples were divided into seven groups depending on the degree of their oxidation and thermal alteration.The PCA findings were verified by other multivariate methods. The relationships among geochemical variables were successfully confirmed by Factor Analysis (FA). The data structure was also described by the Average Group dendrogram using Euclidean distance. The found sample clusters were not defined so clearly as in the case of PCA. It can be explained by the PCA filtration of the data noise.

scholarly journals Spatial and temporal variation of total electron content as revealed by principal component analysis

2016 ◽  
Vol 34 (12) ◽  
pp. 1109-1117 ◽  
Author(s):  
Elsayed R. Talaat ◽  
Xun Zhu
Keyword(s):  
Principal Component Analysis ◽  
Total Electron Content ◽  
General Circulation ◽  
Circulation Model ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variance ◽  
Spatial And Temporal Variation ◽  
Electron Content ◽  
Total Electron

Abstract. Eleven years of global total electron content (TEC) data derived from the assimilated thermosphere–ionosphere electrodynamics general circulation model are analyzed using empirical orthogonal function (EOF) decomposition and the corresponding principal component analysis (PCA) technique. For the daily averaged TEC field, the first EOF explains more than 89 % and the first four EOFs explain more than 98 % of the total variance of the TEC field, indicating an effective data compression and clear separation of different physical processes. The effectiveness of the PCA technique for TEC is nearly insensitive to the horizontal resolution and the length of the data records. When the PCA is applied to global TEC including local-time variations, the rich spatial and temporal variations of field can be represented by the first three EOFs that explain 88 % of the total variance. The spectral analysis of the time series of the EOF coefficients reveals how different mechanisms such as solar flux variation, change in the orbital declination, nonlinear mode coupling and geomagnetic activity are separated and expressed in different EOFs. This work demonstrates the usefulness of using the PCA technique to assimilate and monitor the global TEC field.

scholarly journals Data Mining to Select Relevant Variables Influencing External and Internal Workload of Elite Blind 5-a-Side Soccer

2021 ◽  
Vol 18 (6) ◽  
pp. 3155
Author(s):  
José M. Gamonales ◽  
Kiko León ◽  
Daniel Rojas-Valverde ◽  
Braulio Sánchez-Ureña ◽  
Jesús Muñoz-Jiménez
Keyword(s):  
Data Mining ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Technique ◽  
Total Variance ◽  
Data Mining Technique ◽  
Internal Load ◽  
Relevant Variables ◽  
Data Reduction Method

(1) Background: Data mining has turned essential when exploring a large amount of information in performance analysis in sports. This study aimed to select the most relevant variables influencing the external and internal load in top-elite 5-a-side soccer (Sa5) using a data mining model considering some contextual indicators as match result, body mass index (BMI), scoring rate and age. (2) Methods: A total of 50 top-elite visually impaired soccer players (age 30.86 ± 11.2 years, weight 77.64 ± 9.78 kg, height 178.48 ± 7.9 cm) were monitored using magnetic, angular and rate gyroscope (MARG) sensors during an international Sa5 congested fixture tournament.; (3) Results: Fifteen external and internal load variables were extracted from a total of 49 time-related and peak variables derived from the MARG sensors using a principal component analysis as the most used data mining technique. The principal component analysis (PCA) model explained 80% of total variance using seven principal components. In contrast, the first principal component of the match was defined by jumps, take off by 24.8% of the total variance. Blind players usually performed a higher number of accelerations per min when losing a match. Scoring players execute higher DistanceExplosive and Distance21–24 km/h. And the younger players presented higher HRAVG and AccMax. (4) Conclusions: The influence of some contextual variables on external and internal load during top elite Sa5 official matches should be addressed by coaches, athletes, and medical staff. The PCA seems to be a useful statistical technique to select those relevant variables representing the team’s external and internal load. Besides, as a data reduction method, PCA allows administrating individualized training loads considering those relevant variables defining team load behavior.

scholarly journals Principal Component Analysis of Categorized Polytomous Variable-Based Classification of Diabetes and Other Chronic Diseases

2019 ◽  
Vol 16 (19) ◽  
pp. 3593 ◽  
Author(s):  
Musa Uba Muhammad ◽  
Ren Jiadong ◽  
Noman Sohail Muhammad ◽  
Munawar Hussain ◽  
Irshad Muhammad
Keyword(s):  
Diabetes Mellitus ◽  
Principal Component Analysis ◽  
Chronic Diseases ◽  
Economic Status ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variance ◽  
Classification Techniques ◽  
Polytomous Variable

A chronic disease diabetes mellitus is assuming pestilence proportion worldwide. Therefore prevalence is important in all aspects. Researchers have introduced various methods, but still, the improvement is a need for classification techniques. This paper considers data mining approach and principal component analysis (PCA) techniques, on a single platform to approaches on the polytomous variable-based classification of diabetes mellitus and some selected chronic diseases. The PCA result shows eigenvalues, and the total variance is explained for the principal components (PCs) solution. Total of twelve attributes was analyzed with the intention to precise the pattern of the correlation with minimum factors as possible. Usually, factors with large eigenvalues retained. The first five components have their eigenvalues large enough to be retained. Their variances are 18.9%, 14.0%, 13.6%, 10.3%, and 8.6%, respectively. That explains ~65.3% of the total variance. We further applied K-means clustering with the aid of the first two PCs. As well, correlation results between diabetes mellitus and selected diseases; it has revealed that diabetes patients are more likely to have kidney and hypertension. Therefore, the study validates the proposed polytomous method for classification techniques. Such a study is important in better assessment on low socio-economic status zone regions around the globe.

scholarly journals New Approach to Generalized Two-Dimensional Correlation Spectroscopy. 1: Combination of Principal Component Analysis and Two-Dimensional Correlation Spectroscopy

2002 ◽  
Vol 56 (12) ◽  
pp. 1562-1567 ◽  
Author(s):  
Young Mee Jung ◽  
Hyeon Suk Shin ◽  
Seung Bin Kim ◽  
Isao Noda
Keyword(s):  
Principal Component Analysis ◽  
Correlation Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Correlation Spectroscopy ◽  
Data Matrix ◽  
Two Dimensional ◽  
Noise Component ◽  
Reconstructed Data ◽  
2D Correlation Analysis

The direct combination of chemometrics and two-dimensional (2D) correlation spectroscopy is considered. The use of a reconstructed data matrix based on the significant scores and loading vectors obtained from the principal component analysis (PCA) of raw spectral data is proposed as a method to improve the data quality for 2D correlation analysis. The synthetic noisy spectra were analyzed to explore the novel possibility of the use of PCA-reconstructed spectra, which are highly noise suppressed. 2D correlation analysis of this reconstructed data matrix, instead of the raw data matrix, can significantly reduce the contribution of the noise component to the resulting 2D correlation spectra.

scholarly journals Evaluation of structure in Pinus halepensis M. stands in North Greece

1997 ◽  
Vol 62 ◽  
Author(s):  
D. Karamanolis ◽  
G. Stamatelos ◽  
P. Gkanatsas
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Stand Structure ◽  
Pinus Halepensis ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variance ◽  
Multivariate Technique ◽  
New Variables

The  Principal Component Analysis (P.C.A.) is a multivariate technique useful in  the description and    the revealing of relations between variables in a great number of data. The  structure of Pinus    halepensis forests by P.C.A. was studied. The  method was applied in silvicultural data of Pinus    halepensis forests in Kassandra Peninsula.  Sampling was done on 49 plots spreaded over of the    peninsula. By the analysis of a total of 12 initial variables it was found  that the first 6 principal    components, new variables, interpret almost 83% of the total variance. It  was also found that the    first component, which explains 29.6%, affects the configuration of stand  structure.

scholarly journals Evaluation of soil fertility in the succession of karst rocky desertification using principal component analysis

Solid Earth ◽  
2015 ◽  
Vol 6 (2) ◽  
pp. 515-524 ◽  
Author(s):  
L. W. Xie ◽  
J. Zhong ◽  
F. F. Chen ◽  
F. X. Cao ◽  
J. J. Li ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Microbial Biomass ◽  
Soil Fertility ◽  
Principal Component ◽  
Component Analysis ◽  
Data Matrix ◽  
Karst Rocky Desertification ◽  
Rocky Desertification ◽  
Microbial Biomass Nitrogen ◽  
Changing Trend

Abstract. Expanding of karst rocky desertification (RD) area in southwestern China is strangling the sustainable development of local agricultural economy. It is important to evaluate the soil fertility at RD regions for the sustainable management of karst lands. The changes in 19 different soil fertility-related variables along a gradient of karst rocky desertification were investigated in five different counties belonging to the central Hunan province in China. We used principal component analysis method to calculate the soil data matrix and obtained a standardized integrate soil fertility (ISF) indicator to reflect RD grades. The results showed that the succession of RD had different impacts on soil fertility indicators. The changing trend of total organic carbon (TOC), total nitrogen (TN), available phosphorus, microbial biomass carbon (MBC), and microbial biomass nitrogen (MBN) was potential RD (PRD) > light RD (LRD) > moderate RD (MRD) > intensive RD (IRD), whereas the changing trend of other indicators was not entirely consistent with the succession of RD. The degradation trend of ISF was basically parallel to the aggravation of RD, and the strength of ISF mean values were in the order of PRD > LRD > MRD > IRD. The TOC, MBC, and MBN could be regarded as the key indicators to evaluate the soil fertility.

scholarly journals Social Effect Analysis of Intelligent Sports Based on Principal Component Analysis and Fuzzy Control

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xiaobo Chen
Keyword(s):  
Principal Component Analysis ◽  
Fuzzy Control ◽  
Principal Component ◽  
Interval Data ◽  
Component Analysis ◽  
Data Matrix ◽  
Fuzzy Data ◽  
Social Effect ◽  
Effect Analysis ◽  
The Masses

In order to explore the social effects of intelligent sports systems, this paper combines principal component analysis technology and fuzzy control technology to construct an intelligent sports social effect analysis system. The original fuzzy data is expressed linearly by structural elements, and the original fuzzy data matrix is divided into a main data matrix part and an error data matrix part. According to the principal component analysis method of fuzzy data represented by structural elements, this paper studies the principal component analysis method of interval data using the left end point matrix, right end point matrix, and midpoint matrix of interval data. In addition, this article uses principal component analysis and fuzzy control to study the response of the intelligent motion system to the masses and conducts experiments to analyze the social effects. It can be seen from experimental research that the intelligent sports system constructed in this article has a high degree of satisfaction of the masses, which proves that the intelligent sports have a certain social effect.

scholarly journals Comparing global seismic tomography models using varimax principal component analysis

Solid Earth ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 1601-1634
Author(s):  
Olivier de Viron ◽  
Michel Van Camp ◽  
Alexia Grabkowiak ◽  
Ana M. G. Ferreira
Keyword(s):  
Principal Component Analysis ◽  
Seismic Tomography ◽  
Lower Mantle ◽  
Model Comparison ◽  
Principal Component ◽  
Shear Velocity ◽  
Component Analysis ◽  
Total Variance ◽  
Low Velocity ◽  
Additional Tool

Abstract. Global seismic tomography has greatly progressed in the past decades, with many global Earth models being produced by different research groups. Objective, statistical methods are crucial for the quantitative interpretation of the large amount of information encapsulated by the models and for unbiased model comparisons. Here we propose using a rotated version of principal component analysis (PCA) to compress the information in order to ease the geological interpretation and model comparison. The method generates between 7 and 15 principal components (PCs) for each of the seven tested global tomography models, capturing more than 97 % of the total variance of the model. Each PC consists of a vertical profile, with which a horizontal pattern is associated by projection. The depth profiles and the horizontal patterns enable examining the key characteristics of the main components of the models. Most of the information in the models is associated with a few features: large low-shear-velocity provinces (LLSVPs) in the lowermost mantle, subduction signals and low-velocity anomalies likely associated with mantle plumes in the upper and lower mantle, and ridges and cratons in the uppermost mantle. Importantly, all models highlight several independent components in the lower mantle that make between 36 % and 69 % of the total variance, depending on the model, which suggests that the lower mantle is more complex than traditionally assumed. Overall, we find that varimax PCA is a useful additional tool for the quantitative comparison and interpretation of tomography models.

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