Use Of Principal Component Analysis Research Articles

Multivariate Exploratory Comparative Analysis of LaLiga Teams: Principal Component Analysis.

The use of principal component analysis (PCA) provides information about the main characteristics of teams, based on a set of indicators, instead of displaying individualized information for each of these indicators. In this work we have considered reducing an extensive data matrix to improve interpretation, using PCA. Subsequently, with new components and with multiple linear regression, we have carried out a comparative analysis between the best and bottom teams of LaLiga. The sample consisted of the matches corresponding to the 2015/16, 2016/17 and 2017/18 seasons. The results showed that the best teams were characterized and differentiated from bottom teams in the realization of a greater number of successful passes and in the execution of a greater number of dynamic offensive transitions. The bottom teams were characterized by executing more defensive than offensive actions, showing fewer number of goals and a greater ball possession time in the final third of the field. Goals, ball possession time in the final third of the field, number of effective shots and crosses are the main discriminating performance factors of football. This information allows us to increase knowledge about the key performance indicators (KPI) in football.

Use of Principal Component Analysis to Combine Genetic Merit for Heat Stress and for Fat and Protein Yield in Spanish Autochthonous Dairy Goat Breeds.

Simple SummaryTolerance to heat stress (HS) is an important economic trait in goats to maintain dairy farm profitability. We studied the effect of a climatological index of temperature and relative humidity (THI) on test day record (TD) of fat and protein yield (fpy) in the three main Spanish dairy goat breeds (Florida, 126.825 TD, Malagueña, 141.856 TD and Murciano Granadina, 62.834 TD), analysing the nature of the fpy response throughout the THI on the weeks of lactation (DIM) trajectories. The results showed the existence of a double genotype environment interaction between DIM and THI and a depression in the fpy level in animals kept in the hot zone (THI > 25) compared with those in the cold zone (THI ≤ 16). This negative impact is equivalent to 13 to 30 days in production. We propose an alternative to select animals by using a principal analysis of the estimated breeding value for each goat breed across DIM and THI scales. The results of this study show that this procedure is a powerful tool to select the most productive and heat-tolerant animals, thus contributing to the increased profitability of autochthonous Spanish goat breeds.We studied the effect of the Temperature Humidity Index (THI) (i.e., the average of temperature and relative humidity registered at meteorological stations) closest to the farms taken during the test day (TD), for total daily protein and fat yields (fpy) of the three main Spanish dairy goats. The data were from Florida (11,244 animals and 126,825 TD), Malagueña (12,215 animals and 141,856 TD) and Murciano Granadina (5162 animals and 62,834 TD) breeding programs and were studied by different linear models to estimate the nature of the fpy response throughout the THI and the weeks of lactation (Days in Milk, DIM) trajectories. The results showed an antagonism between THI and DIM, with a marked depression in the fpy level in animals kept in the hot zone of the THI values (THI > 25) compared with those in the cold zone (THI ≤ 16), with a negative impact equivalent to production of 13 to 30 days. We used a Reaction Norm model (RN), including THI and DIM as fixed covariates and a Test Day Model (TDM), to estimate the genetic (co)variance components. The heritability and genetic correlations estimated with RN and TDM showed a decreased pattern along the scale of THI and DIM, with slight differences between breeds, meaning that there was significant genetic variability in the animal’s ability to react to different levels of THI, which is not constant throughout the DIM, showing the existence of genotype-environment interaction. The breeding values (BV) of all animals for each level of THI and DIM were subject to a principal component analysis, and the results showed that 89 to 98% of the variance between the BV was explained by the two first eigenvalues. The standardized BV were weighted with the corresponding eigenvector coefficients to construct an index that showed, in a single indicator, the most complete expression of the existing genetic variability in the animals’ ability to produce fpy along the trajectories of THI and DIM. This new option will make it easier to select animals which are more productive, and with better adaptability to heat stress, as well as enabling us to exploit genetic variations in the form of the response to heat stress to be adapted to different production systems.

Shakespeare and principal components analysis

Abstract The use of principal components analysis (PCA) in literary studies was pioneered by John Burrows. From him it was adopted by the New Oxford Shakespeare project team, who used it to support their controversial attributions of parts of Arden of Faversham to Shakespeare and parts of the Henry VI trilogy to Marlowe. This essay examines a representative sample of the PCA tests done using function words by that team. It observes that, as with their Zeta method, they used the unsound bisector line method of interpretation. By calculating information which they did not disclose, it finds that their decision to discard all but two principal components was not justified by the data they were using, putting into doubt their interpretations of the results. It shows that their test method is vulnerable by design to the making of false attributions and demonstrates this by a new experiment. By a further experiment, it shows that the PCA tests may be telling us that differences between characters are greater than those between authors, at least in the use of function words. It concludes that the PCA tests done by that project cannot be relied on; and, given the already demonstrated unreliability of their Zeta tests, these new findings invalidate all work presented in the influential book Shakespeare, Computers, and the Mystery of Authorship.

An Analysis of Electricity Congestion Price Patterns in North America

The current paper illustrates the use of principal component analysis (PCA) on the congestion component of local (i.e. zonal) electricity price data to detect the most salient congestion patterns in electricity transmission grids managed by either a Regional Transmission Organization (RTO) or an Independent System Operator (ISO). Outputs from the PCA along with some data visualization tools are shown to make the identification of such patterns seamless and straightforward. An empirical analysis is conducted for three North American power systems, namely NYISO, ISO New England and PJM.

First insights into the archaeometric analysis of the Los Amores Mosaic in C\xe1stulo (Linares, Spain): the Judgement of Paris

This paper discusses results obtained from in situ analysis of the tesserae of the Roman mosaic of Los Amores (Cástulo site, Linares, Spain) dating back to the turn of the 1st to the 2nd century AD. Specifically, it focuses on the scene The Judgment of Paris. In view of the exceptional state of preservation of the mosaic, from which very few tesserae had fallen off, non-invasive methods with portable Micro Raman Spectroscopy (MRS) and hand-held X-ray fluorescence (hXRF) and data assessment by use of principal component analysis and binary representations were selected. The results obtained allow to evaluate both the analytical method and the portable equipment used, as well as to classify the raw materials, the colouring agents and the opacifiers used. MRS analysis proved crucial for the identification of stone tesserae (ironstones, carbonate and siliciclastic rocks) and for the identification of the type of glasses used (soda-lime-silicate and lead type glasses) based on the analysis of two detached tesserae. hXRF analysis of the glass tesserae identified both colouring agents (Co, Cu, Pb, Zn) and opacifiers (calcium antimonate). The data obtained lend themselves to an assessment of the degradation process that threaten the integrity of the mosaic. The identification of tessera made of specific stone materials (especially ironstone) and of lead glass tesserae suggest the existence of a mosaic workshop in the Upper Guadalquivir (Eastern Andalusia, Spain).

Using geographically weighted regression to predict the spatial distribution of frozen ground temperature: a case in the Qinghai–Tibet Plateau

This paper combines the use of principal component analysis (PCA) and the geographically weighted regression (GWR) model to predict the spatial distribution of frozen ground temperature. PCA is used to reduce the multicollinearity among covariates, while the GWR model is used to address the spatially non-stationary relationship between frozen ground temperature and its predictors, such as air temperature, latitude, longitude, and vegetation cover. Our approach is applied in a typical permafrost area in the Qinghai–Tibet Plateau, Western China. The result demonstrates the applicability of our approach in the spatial distribution of frozen ground temperature and shows that the approach can be used for analysis and prediction. This study offers insight into temperature monitoring of frozen ground, which helps prevent regional geological disasters.

Using Principal Component Analysis to Create Prognostic Cytokine Scores for Adverse Outcome in Acute Myocardial Infarction

Inflammatory biomarkers following acute myocardial infarction (AMI) may predict major adverse cardiovascular events (MACE). However, the optimal way of measuring the complex inflammatory response following AMI remains unknown. We explored the use of principal component analysis (PCA) to generate combined scores of multiple inflammatory cytokines that might be predictive of MACE.

Use of the Principal Component Analysis for Classification of Aircraft Components Failure Conditions Using Vibrodiagnostics

Safety and trouble-free operation are important factors that places aviation among the safest forms of transport. These parameters are achieved mainly by state-of-the-art diagnostic and maintenance programs and methods, which are constantly evolving and increasing their efficiency. The manuscript is focused on the use of a centrifugal blower, on which fault states were artificially simulated and subsequently analyzed by statistical methods not normally used in the field of vibrodiagnostics. The use of principal component analysis (PCA) method played a key role in evaluating the vibrodiagnostic signal of the measured device. The PCA method enabled the categorization of fault conditions and provided a unique view of diagnostic methods with reference to specific signals. The results of the presented research indicate the potential of the PCA method in practice and in the need to carry out further research for other fault conditions. The implementation of such a tool is a prerequisite for more effective determination of specific fault conditions and the use of the PCA method in vibrodiagnostics not only in aviation but also in general industry.

Detection of Forest Fire Damage from Sentinel-1 SAR Data through the Synergistic Use of Principal Component Analysis and K-means Clustering

Detection of Forest Fire Damage from Sentinel-1 SAR Data through the Synergistic Use of Principal Component Analysis and K-means Clustering

A Choice of Representative Sections for the Estimation of the Transport Network Loading

The problem of a choice of representative sections for transport network loading management is considered in the following article. To solve the problem, the use of Principal component analysis and the procedures of cluster analysis are offered. The example of the choice of representative sections at network traffic control is presented.

Simultaneous determination of organic acids and sugars in fruit juices by High performance liquid chromatography: characterization and differentiation of commercial juices by principal component analysis

ABSTRACT: The present research presents an analytical methodology based on High Performance Liquid Chromatography (HPLC) and Principal Component Analyses (PCA) for simultaneous quantification and analytical differentiation of organic acids and sugars in commercial fruit juice samples (orange, grape, apple and tangerine). In addition to the development of the method that generated suitable validation paramters for quantitative analytical applications, the analysis of fourteen commercial samples and the use of Principal Component Analysis indicated the relationship between the constituents and the very constitutional chemical nature of the juice. In general, in grape and apple juices, ascorbic acid was not quantified and the citric acid content was very low; however, the concentrations of fructose and glucose were the highest in both juices. In orange and mandarin orange juices, the content of all analytes, except acorbic acid, did not differ statistically from each other. However, these differed significantly from the others, mainly in relation to sucrose content. Finally, the apple juice samples differed according to the content of fructose and malic acid, the predominant constituint of the apple. Results showed that the simultaneous chromatographic method associated with principal component analysis generated important information about characteristics of commercial juices, with the potential to be used in systems of quality control and identification of adulterations.

Application of the principal component analysis (PCM) as an innovative technology for assessing the morphological and functional state of agricultural poultry with diseases of bacterial genesis

The possibility of using the method of principal components (PCM) for analyzing the results of hematological studies of blood samples of broiler chickens has been studied. It was found that the graphs of the residual dispersion show a clear clustering of samples into the control and experimental groups after infection in the PC1-PC2 space. The graphs of the scores, built for the higher BG models, showed changes in the values for the GC1 spectrum of leukocytes, and for the GC2 of erythrocytes, hemoglobin. Thus, the use of PCM analysis of hematological blood data makes it possible to separate blood samples from healthy broiler chickens and those susceptible to infection with various infections. PCM is a promising approach for rapid assessment of hematological data.

USED EIGENVALUES AND EIGENVECTORS TO COMPRESS THE IMAGE

The article’s main aim is to point out the significant applications of the linear algebra in the medical engineering field. Hence, the eigenvectors and eigenvalues which represent the core of linear algebra are discussed in details in order to show how they can be used in many engineering applications. The principal components analysis is one of the most important compression and feature extraction algorithms used in the engineering field (1). It mainly depends on the calculation and extraction of eigenvalues and eigenvectors that then used to represent an input; whether it's the image or a simple matrix. In this article, the use of principal component analysis for medical image compression is an important and novel application of linear algebra (2).

Convolutional Neural Networks with Transfer Learning for Recognition of COVID-19: A Comparative Study of Different Approaches

To judge the ability of convolutional neural networks (CNNs) to effectively and efficiently transfer image representations learned on the ImageNet dataset to the task of recognizing COVID-19 in this work, we propose and analyze four approaches. For this purpose, we use VGG16, ResNetV2, InceptionResNetV2, DenseNet121, and MobileNetV2 CNN models pre-trained on ImageNet dataset to extract features from X-ray images of COVID and Non-COVID patients. Simulations study performed by us reveal that these pre-trained models have a different level of ability to transfer image representation. We find that in the approaches that we have proposed, if we use either ResNetV2 or DenseNet121 to extract features, then the performance of these approaches to detect COVID-19 is better. One of the important findings of our study is that the use of principal component analysis for feature selection improves efficiency. The approach using the fusion of features outperforms all the other approaches, and with this approach, we could achieve an accuracy of 0.94 for a three-class classification problem. This work will not only be useful for COVID-19 detection but also for any domain with small datasets.

Large Dataset Classification Using Parallel Processing Concept

Much attention has been paid to large data technologies in the past few years mainly due to its capability to impact business analytics and data mining practices, as well as the possibility of influencing an ambit of a highly effective decision-making tools. With the current increase in the number of modern applications (including social media and other web-based and healthcare applications) which generates high data in different forms and volume, the processing of such huge data volume is becoming a challenge with the conventional data processing tools. This has resulted in the emergence of big data analytics which also comes with many challenges. This paper introduced the use of principal components analysis (PCA) for data size reduction, followed by SVM parallelization. The proposed scheme in this study was executed on the Spark platform and the experimental findings revealed the capability of the proposed scheme to reduce the classifiers’ classification time without much influence on the classification accuracy of the classifier.

Two centuries of painted plasters from the Lateran suburban villa (Rome): investigating supply routes and manufacturing of pigments

• Combined non-destructive analytical approach using portable instrumentation • Complementary techniques and multivariate analysis allow characterization of pigments • Improved knowledge on pigments production, sourcing and use in 27 BC – 193 AD Rome Polychrome painted plasters from a suburban villa of Imperial epoch were recovered during the 1930s, and are currently exhibited in the galleries underneath the St. John in Lateran Church, Rome. As wall paintings from the Rome area are a rare find, and as the fragments were never subjected to conservation treatments, it was chosen to non-invasively characterize 113 coloured areas, representative of colours and decorative systems. The chemical and mineralogical information collected from this measurement campaign were interpreted in the light of archaeological context (i.e. the high status of the family who owned the villa , the different renovation works that affected it, the identification of decorative systems linked to such works), as well as of published data from contemporary wall paintings in the Rome area. A multi-analytical, non-destructive characterization of the painted surfaces was carried out in situ by means of portable Raman spectroscopy (pRS) and X-ray fluorescence (pXRF), in the framework of a wider project concerning the study of raw materials and paintings of archaeological interest. The combination of pXRF and pRS allowed to characterize the whole palette, as well as to discriminate among different pigments sharing the same key element, and to identify degradation issues. In this context, the use of principal component analysis gave an important contribution to the interpretation of data ant to the individuation and characterization of pigments such as ochres, green earths, lead based pigments, Egyptian blue, cinnabar, Tyrian purple. Calcium and magnesium carbonates are present, as expected, in the plaster, and are often associated with their sulphates and nitrates, and Ca-oxalates indicating degradation. Lead white and bone white have been used. Some peculiar yellow pigments have been identified, such as realgar/pararealgar and Naples’ yellow, in addition to ochres. Hematite, minium and cinnabar were all identified, alone or in mixtures. The presence of the precious dye Tyrian purple is very likely, but could not be confirmed univocally. Green earths and complex mixtures of iron and copper compounds were detected. Egyptian blue was used, highlighting different production processes for this artificial glassy material. For the blacks, we hypothesize the use of carbon-based pigments. The obtained data confirms the general palette used by Roman painters, and sheds new light on the materials used in the Lateran area across two centuries, including their provenance and manufacturing. It appears that different earths and ochres were used diachronically, and that the owners could afford very expensive pigments, such as cinnabar and Tyrian purple.

Environmental Impact Assessment of Taza City's Wastewater: Application through Principal Component Analysis.

Taza City is among the Moroccan cities which is in full urban expansion, with a daily wastewater discharge volume estimated at 16534 m3/d in 2020, and expected to reach 20056 m3/d by 2030. These waters, collected in a combined sewerage network, are directly released into the natural environment without any treatment. Indeed, a large part of this water is discharged into Oued Defali, the main tributary of Oued Larbâa. In order to manage and better understand these discharges impact on the streams crossing this city, wastewater sampling campaigns were carried out for one year from May 2018 to April 2019 at domestic (S1) and industrial (S2) sites. The wastewater physicochemical characterization revealed that these discharges are highly loaded with organic matter in terms of chemical oxygen demand (S1 avg = 1231.44 mg/l and S2 avg = 933.03 mg/l), biochemical oxygen demand (S1 avg = 511.87 mg/l and S2 avg = 464.35 mg/l), and suspended matter (S1 avg = 744.11 mg/l and S2 avg = 578.13 mg/l). The use of principal component analysis (PCA) has allowed us to collect as much information as possible from the database of the physicochemical analyses performed for the studied parameters.

A Systematic Review of Methods and Criteria Standard Proposal for the Use of Principal Component Analysis in Team's Sports Science.

The availability of critical information about training and competition is fundamental on performance. Principal components analysis (PCA) is widely used in sports as a multivariate technique to manage big data from different technological assessments. This systematic review aimed to explore the methods reported and statistical criteria used in team’s sports science and to propose a criteria standard to report PCA in further applications. A systematic electronic search was developed through four electronic databases and a total of 45 studies were included in the review for final analysis. Inclusion criteria: (i) of the studies we looked at, 22.22% performed factorability processes with different retention criteria (r > 0.4–0.7); (ii) 21 studies confirmed sample adequacy using Kaiser-Meyer-Olkim (KMO > 5–8) and 22 reported Bartlett’s sphericity; (iii) factor retention was considered if eigenvalues >1–1.5 (n = 29); (iv) 23 studies reported loading retention (>0.4–0.7); and (v) used VariMax as the rotation method (48.9%). A lack of consistency and serious voids in reporting of essential methodological information was found. Twenty-one items were selected to provide a standard quality criterion to report methods sections when using PCA. These evidence-based criteria will lead to a better understanding and applicability of the results and future study replications.

Application of machine learning techniques in operating parameters prediction of Stirling cryocooler

The Stirling cryocoolers are widely used in the military and the aerospace fields due to many advantages such as high efficiency and compact structure. The output performance is affected by three parameters: compressor stroke, expander stroke, and the phase shift between them. How to quickly and effectively adjust the three parameters to meet the required cooling capacity with a higher COP is of great significance to the actual engineering application. When the cooling demand is changed, it takes a long time to calculate the corresponding high-efficiency operating parameters using the Stirling cryocooler simulation model on the market. In this paper, the prediction models of the optimal combination of operating parameters based on particle swarm optimization and commonly used machine learning techniques of back propagation neural network, support vector regression and random forest regression are established for the Stirling cryocooler. Besides, the impact of the two data preprocessing methods (min–max normalization and principal component analysis) on the PV power models and compressor stroke regression models based on three different machine learning techniques is analyzed, which shows that the use of principal component analysis can significantly enhance the performance of the back propagation neural network, and the normalization of the cryocooler parameters can improve the convergence of the support vector regression and the random forest regression. The results show that the regression models established by the three machine learning algorithms all have high accuracy and generalization. The regression model based on support vector machine has the best performance on the prediction of PV power and compressor stroke with small mean squared error, mean absolute error, average relative error, and high Pearson correlation coefficient. In addition, the research on the prediction models for the output performance is beneficial to propose a new control strategy, so as to improve the control system of the Stirling cryocooler.

Feature Selection for Monitoring Erosive Cavitation on a Hydroturbine

This paper presents a method for comparing and evaluating cavitation detection features - the first step towards estimating remaining useful life (RUL) of hydroturbine runners that areimpacted by erosive cavitation. The method can be used to quickly compare features created from cavitation survey data collected on any type of hydroturbine, sensor type, sensor location, and cavitation sensitivity parameter (CSP). Although manual evaluation and knowledge of hydroturbine cavitation is still required for our feature selection method, the use of principal component analysis greatly reduces the number of plots that require evaluation. We present a case study based on a cavitation survey data collected on a Francis hydroturbine located at a hydroelectric plant and demonstrate the selection of the most advantageous sensor type, sensor location, and CSP to use on this hydroturbine for long-term monitoring of erosive cavitation. Our method provides hydroturbine operators and researchers with a clear and effective means to determine preferred sensors, sensor placements, and CSPs while also laying the groundwork for determining RUL in the future.