Common Principal Component Analysis Research Articles

Cognitive performance from childhood to old age and intergenerational correlations in the multigenerational Young Finns Study

BackgroundCognitive performance changes during the lifespan, but the information is gathered from studies on separate age cohorts. Computerized neurocognitive testing enables efficient and similar assessments for all ages. We investigated (i) the effect of age at different stages of life and (ii) intergenerational correlations across cognitive domains in the multigenerational Young Finns Study.MethodsParticipants in three familiarly related generations (n = 6486, aged 7–92 years) performed the Cambridge Neuropsychological Test Automated Battery (CANTAB). Overall cognitive performance and domains representing learning and memory, working memory, information processing, and reaction time were extracted by common principal component analysis from the cognitive data with several age groups. Linear models were used to study the association of age, sex, and education with overall cognitive performance and in the cognitive domains. Age-adjusted intergenerational correlations were calculated.ResultsLearning and memory peaked earlier during the lifespan compared to working memory and information processing, and the rate of decline toward old age differed by domain. Weak intergenerational correlations existed between two consecutive generations but were nonsignificant between grandparents and grandchildren. There was no systematic sex-specific transmission in any cognitive domain.ConclusionThis study describes the natural course of cognitive performance across the lifespan and proves that cognitive performance changes differently across cognitive domains with weak intergenerational transmission.

Change in cognitive performance during seven-year follow-up in midlife is associated with sex, age, and education – The Cardiovascular Risk in Young Finns Study

ObjectiveSex, age, and education are associated with the level of cognitive performance. We investigated whether these factors modulate the change in cognitive performance in midlife by leveraging the longitudinal data from the Cardiovascular Risk in Young Finns Study (YFS).MethodsParticipants of the YFS cohort performed a computer-based Cambridge Neuropsychological Test Automated Battery (CANTAB) in 2011 and 2018 (n = 1671, age 41–56 years in 2018). Overall cognitive performance and domains representing learning and memory, working memory, reaction time, and information processing were extracted by common principal component analysis from the longitudinal cognitive data. Linear models adjusted for baseline cognitive performance were used to study the association of sex, age, and education with changes in overall cognitive performance and in the cognitive domains.ResultsCognitive performance decreased in all domains (overall cognition -0.56 SD, p < 0.001; working memory -0.81 SD, p < 0.001; learning and memory -0.70 SD, p < 0.001; reaction time -0.06 SD, p = 0.019; information processing -0.03 SD, p = 0.016). The decrease in working memory and information processing was greater in females compared to males. Cognitive performance decreased more in older participants in all domains. Education alleviated the decrease in cognitive performance in all domains except reaction time. The beneficial effect of education was greater for males.ConclusionsThis study describes the natural course of aging-related changes in cognitive performance in midlife, the critical time window for early prevention of clinical cognitive decline. These findings provide a reference for studies focusing on determinants of pathological cognitive decline deviating from normal changes in cognitive performance.

Filtrated common functional principal component analysis of multigroup functional data

Filtrated common functional principal component analysis of multigroup functional data

Research on Risk Assessment and Prediction of RMB Internationalization Based on the PCA-SA-BPNN Model.

This paper combines principal component analysis, a BP neural network, and a simulated annealing algorithm, to construct a PCA-SA-BPNN risk forecast model to evaluate and predict the RMB internationalization risk status of China. First, we analyze the risk of RMB internationalization and its transmission mechanism from the perspective of the economic characteristics of neighboring countries and trading partner countries. Second, we use the FASP index system construction method for reference to construct a forecast index system for macro- and microrisks brought about by RMB internationalization. Then, the weight of each index is determined through index common degree analysis and principal component analysis, and the risk of RMB internationalization is divided. On this basis, the risks of RMB internationalization in China from 2000 to 2019 are divided into four categories. Based on the BP neural network algorithm optimized by the simulated degradation algorithm, the PCA-SA-BPNN model of RMB internationalization risk forecast is constructed. Finally, the validity of the model is verified by experimental verification, and the risk status of RMB internationalization in 2020 is simulated and predicted. The research results show that the risk status of RMB internationalization in 2020 is basically safe, and the risks of RMB internationalization mainly come from macroeconomic growth risks and systemic risks of the financial system.

Nonstationary and Multirate Process Monitoring by Using Common Trends and Multiple Probability Principal Component Analysis

Process monitoring plays a critical role in guaranteeing the stability and safety of the industrial process. To obtain sufficient information from industrial processes, the measurements usually con...

Comparison of dimension reduction methods on fatty acids food source study

Serum fatty acids (FAs) exist in the four lipid fractions of triglycerides (TGs), phospholipids (PLs), cholesteryl esters (CEs) and free fatty acids (FFAs). Total fatty acids (TFAs) indicate the sum of FAs in them. In this study, four statistical analysis methods, which are independent component analysis (ICA), factor analysis, common principal component analysis (CPCA) and principal component analysis (PCA), were conducted to uncover food sources of FAs among the four lipid fractions (CE, FFA, and TG + PL). Among the methods, ICA provided the most suggestive results. To distinguish the animal fat intake from endogenous fatty acids, FFA variables in ICA and factor analysis were studied. ICA provided more distinct suggestions of FA food sources (endogenous, plant oil intake, animal fat intake, and fish oil intake) than factor analysis. Moreover, ICA was discovered as a new approach to distinguish animal FAs from endogenous FAs, which will have an impact on epidemiological studies. In addition, the correlation coefficients between a published dataset of food FA compositions and the loading values obtained in the present ICA study suggested specific foods as serum FA sources. In conclusion, we found that ICA is a useful tool to uncover food sources of serum FAs.

Identification of Two Commercial Pesticides by a Nanoparticle Gas-Sensing Array.

This study presents the experimental testing of a gas-sensing array, for the detection of two commercially available pesticides (i.e., Chloract 48 EC and Nimrod), towards its eventual use along a commercial smart-farming system. The array is comprised of four distinctive sensing devices based on nanoparticles, each functionalized with a different gas-absorbing polymeric layer. As discussed herein, the sensing array is able to identify as well as quantify three gas-analytes, two pesticide solutions, and relative humidity, which acts as a reference analyte. All of the evaluation experiments were conducted in close to real-life conditions; specifically, the sensors response towards the three analytes was tested in three relative humidity backgrounds while the effect of temperature was also considered. The unique response patterns generated after the exposure of the sensing-array to the two gas-analytes were analyzed using the common statistical analysis tool Principal Component Analysis (PCA). The sensing array, being compact, low-cost, and highly sensitive, can be easily integrated with pre-existing crop-monitoring solutions. Given that there are limited reports for effective pesticide gas-sensing solutions, the proposed gas-sensing technology would significantly upgrade the added-value of the integrated system, providing it with unique advantages.

Enhancing the feature-based 3D deformable face recognition using hybrid PCA-NN

&lt;p&gt;Facial recognition is one of the most important advancements in image processing. An important job is to build an automated framework with the same human capacity’s for recognizing face. The face is a complex 3D graphical model, and constructing a computational model is a challenging task. This paper aims at a facial detection technique focused on the coding and decoding of the facial feature object theory approach to data. One of the most natural and common principal component analysis (PCA) method. This approach transforms the face features into a minimal set of basic attributes, peculiarities, which are the critical components of the original learning image collection (or the training package). The proposed technique is a combination of the PCA system and the identification of components using the neural network (NN) feed-forward propagation method. This experiment proves that recognition of deformed 3D face is doable. By taking into account almost all forms of feature extraction and engineering, the NN yields a recognition score of 95%.&lt;/p&gt;

Semiparametric partial common principal component analysis for covariance matrices.

We consider the problem of jointly modeling multiple covariance matrices by partial common principal component analysis (PCPCA), which assumes a proportion of eigenvectors to be shared across covariance matrices and the rest to be individual-specific. This paper proposes consistent estimators of the shared eigenvectors in the PCPCA as the number of matrices or the number of samples to estimate each matrix goes to infinity. We prove such asymptotic results without making any assumptions on the ranks of eigenvalues that are associated with the shared eigenvectors. When the number of samples goes to infinity, our results do not require the data to be Gaussian distributed. Furthermore, this paper introduces a sequential testing procedure to identify the number of shared eigenvectors in the PCPCA. In simulation studies, our method shows higher accuracy in estimating the shared eigenvectors than competing methods. Applied to a motor-task functional magnetic resonance imaging data set, our estimator identifies meaningful brain networks that are consistent with current scientific understandings of motor networks during a motorparadigm.

The fixed effects PCA model in a common principal component environment

This paper explores multivariate data using principal component analysis (PCA). Traditionally, two different approaches to PCA have been considered, an algebraic descriptive one and a probabilistic one. Here, a third type of PCA approach, lying somewhere between the two traditional approaches, called the fixed effects PCA model, is considered. This model includes mainly geometrical, rather than probabilistic assumptions, such as the optimal choice of dimensionality and metric. The model is designed to account for any possible prior information about the noise in the data to yield better estimates. Parameters are estimated by minimizing a least-squares criterion with respect to a specified metric. A suggestion of how the fixed effects PCA estimates can be improved in a common principal component (CPC) environment is made. If the CPC assumption is fulfilled, then the fixed effects PCA model can consider more information by incorporating common principal component analysis (CPCA) theory into the estimation procedure.

Multivariate time series clustering based on common principal component analysis

Time series clustering is often applied to pattern recognition and also as the basis of the tasks in the field of time series data mining including dimensionality reduction, feature extraction, classification and visualization. Due to the high dimensionality of multivariate time series and most of the previous work concentrating on univariate time series clustering, a novel method which is based on common principal component analysis, is proposed to achieve multivariate time series clustering more fast and accurately. It is inspired by the traditional clustering method K-Means and can construct a common projection axes as prototype of each cluster. Moreover, the reconstruction error of each multivariate time series projected on the corresponding common projection axes are used to reassign the member of the cluster. The detailed algorithm of the proposed method Mc2PCA is given and the time complexity is analyzed, which shows that the proposed method is very fast and its time complexity is linear to the number of multivariate time series objects. Unlike the traditional methods, the proposed method considers the relationship among variables and the distribution of the original data values of multivariate time series. The experimental results in the various datasets demonstrate that Mc2PCA is superior to the traditional methods for multivariate time series clustering.

Robust Clustering of Banks in Argentina

The purpose of this paper is to classify and characterize 64 banks, active as of 2010 in Argentina, by means of robust techniques used on information gathered during the period 2001-2010. Based on the strategy criteria established in (Wang 2007) and (Werbin 2010), seven variables were selected. In agreement with bank theory, four “natural” clusters were obtained, named “Personal”, “Commercial”, “Typical” and “Other banks”. In order to understand this grouping, projection pursuit based robust principal component analysis was conducted on the whole set showing that essentially three variables can be attributed the formation of different clusters. In order to reveal each group inner structure, we used R package mclust to fit a finite Gaussian mixture to the data. This revealed approximately a similar component structure, granting a common principal components analysis as in (Boente and Rodrigues, 2002). This allowed us to identify three variables which suffice for grouping and characterizing each cluster. Boente’s influence measures were used to detect extreme cases in the common principal components analysis.

A 1-V 2.6-mW Environmental Compensated Fully Integrated Nose-on-a-Chip

Miniature gas detection system is desirable for many applications. In this brief, we propose a fully integrated nose-on-a-chip for mobile phones and wearable devices. The chip has 36 on-chip sensors, a 36-channel adaptive interface with an integrated programmable amplifier, a four-channel frequency readout interface, one on-chip temperature sensor, a successive approximation analog-to-digital converter, a scalable learning kernel cluster, and a reduced instruction set computing core with low-voltage static random-access memory. This chip is fabricated using the TSMC 180-nm CMOS process and consumes 2.6 mW at 1 V. on-chip temperature and humidity sensors are included in this system for acquiring environmental information. An environmental compensation algorithm based on common principal components analysis is also implemented for high-accuracy odor classification. This fully integrated nose-on-a-chip provides promising results for odor detection and classification for use in mobile and wearable device applications.

On Common Factor and Principal Component Representations of Data: Implications for Theory and for Confirmatory Replications

Common factor analysis (FA) and principal component analysis (PCA) are commonly used to obtain lower-dimensional representations of matrices of correlations among manifest variables. Whereas some experts argue that differences in results from use of FA and PCA are small and relatively unimportant in empirical studies, the fundamental rationales for the two methods are very different. Here, FA and PCA are contrasted on four key issues: the range of possible dimensional loadings, the range of potential correlations among dimensions, the structure of residual covariances and correlations, and the relation between population parameters and the correlational structures with which they are associated. For decades, experts have emphasized indeterminacies of the FA model, particularly indeterminacy of common factor scores. Determinate in most respects, a heretofore unacknowledged, pernicious indeterminacy of PCA is demonstrated: the indeterminacy between PCA structural representations and the correlational structures from which they are derived. Researchers are often advised to use either FA or PCA in exploratory rounds of data analysis to understand and refine the dimensional structure of a domain before moving to Structural Equation Modeling in later theory-testing, confirmatory, replication studies. Results from the current study suggest that PCA is an unreliable method to use for such purposes and may lead to serious misrepresentation of the structure of a domain. Hence, PCA should never be used if the goal is to understand and represent the latent structure of a domain; only FA techniques should be used for this purpose, as only FA provides reliable structural representations as the basis for confirmatory tests in future studies.

Rethinking the Exploration of Dichotomous Data: Mokken Scale Analysis Versus Factorial Analysis

The need to determine the correct dimensionality of theoretical constructs and generate valid measurement instruments when underlying items are categorical has generated a significant volume of research in the social sciences. This article presents two studies contrasting different categorical exploratory techniques. The first study compares Mokken scale analysis (MSA) and two-factor-based exploratory techniques for noncontinuous variables: item factor analysis and Normal Ogive Harmonic Analysis Robust Method (NOHARM). Comparisons are conducted across techniques and in reference to the common principal component analysis model using simulated data under conditions of two-dimensionality with different degrees of correlation ( r = .0 to .6). The second study shows the theoretical and practical results of using MSA and NOHARM (the factorial technique which functioned best in the first study) on two nonsimulated data sets. The nonsimulated data are particularly interesting because MSA was used to solve a theoretical debate. Based on the results from both studies, we show that the ability of NOHARM to detect dimensionality and scalability is similar to MSA when the data comprise two uncorrelated latent dimensions; however, NOHARM is preferable when data are drawn from instruments containing latent dimensions weakly or moderately correlated. This article discusses the theoretical and practical implications of these findings.

Construct a composite indicator based on integrating Common Weight Data Envelopment Analysis and principal component analysis models: An application for finding development degree of provinces in Iran

Balanced development of regions requires the fair distribution of facilities and services. Hence, it is necessary to find and estimate the development degree of regions for policy makers. This paper presents an integrated Common Weight Data Envelopment Analysis (CWDEA) – Principal Component Analysis (PCA) model to find out the development degree of provinces in Iran. First, 131 suitable indicators are selected and then, the indicators are classified in fourteen different classes. In classical DEA model, each Decision Making Unit (DMU) is free to set its weights to reach the efficient frontier. In this paper, to restrict flexibility in indicator weights, development degree of provinces in each class is calculated using CWDEA model. Since, the proposed CWDEA model is not capable of fully ranking of provinces with all indicators, hence, the development degrees generated by CWDEA model are considered as indicators of PCA and the final ranks are obtained using PCA model. The results of proposed CWDEA-PCA model show that Yazd, Semnan and Bushehr are top three provinces in Iran.

Output-Only Damage Identification Using Enhanced Structural Characteristic Deflection Shapes and Adaptive Gapped Smoothing Method

Structural characteristic deflection shapes (CDSs) such as mode shapes which contain spatial knowledge of structures are highly sensitive for damage detection and localization. Nevertheless, CDSs are vulnerable to measurement noise, which degrades the accuracy of damage identification. In order to enhance CDS-based damage identification, contributions are made in three aspects. First, a robust CDS estimation approach is proposed based on common principal component analysis, which estimates the CDSs as the common diagonalizer of a set of covariance matrices by joint approximation diagonalization (JAD). Second, an adaptive gapped smoothing method (GSM) is proposed and validated to be more accurate than the traditional GSM. Third, a new damage identification index capable of localizing damage and indicating relative damage severity is defined without requiring information of healthy structures. Finally, numerical and experimental examples of beams and a frame with cracks are studied to demonstrate the advantages of the proposed damage identification method in terms of noise robustness and accuracy.

Accurate and efficient classification based on common principal components analysis for multivariate time series

Multivariate time series are found everywhere and they are important data in the field of data mining, but their high dimensionality often hinders the quality of techniques employed for classifying multivariate time series. In this study, we propose an accurate and efficient classification method based on common principal components analysis for multivariate time series. First, multivariate time series are divided into several clusters according to the number of class labels, and the high dimensionality of multivariate time series can then be reduced by common principal components analysis, which gives the reduced principal component series sufficiently high variance. Second, each cluster is used to construct the corresponding reduced coordinate space formed by the eigenvectors of the common covariance matrix. Third, any multivariate time series without a class label can be projected onto these coordinate spaces and its label can be predicted based on the minimal variance of the reduced principal components series according to the different projections. Our experimental results demonstrated that the proposed method for the classification of multivariate time series is more accurate and efficient than existing methods. It is also flexible for multivariate time series with different lengths.

A New 4D Trajectory-Based Approach Unveils Abnormal LV Revolution Dynamics in Hypertrophic Cardiomyopathy

The assessment of left ventricular shape changes during cardiac revolution may be a new step in clinical cardiology to ease early diagnosis and treatment. To quantify these changes, only point registration was adopted and neither Generalized Procrustes Analysis nor Principal Component Analysis were applied as we did previously to study a group of healthy subjects. Here, we extend to patients affected by hypertrophic cardiomyopathy the original approach and preliminarily include genotype positive/phenotype negative individuals to explore the potential that incumbent pathology might also be detected. Using 3D Speckle Tracking Echocardiography, we recorded left ventricular shape of 48 healthy subjects, 24 patients affected by hypertrophic cardiomyopathy and 3 genotype positive/phenotype negative individuals. We then applied Generalized Procrustes Analysis and Principal Component Analysis and inter-individual differences were cleaned by Parallel Transport performed on the tangent space, along the horizontal geodesic, between the per-subject consensuses and the grand mean. Endocardial and epicardial layers were evaluated separately, different from many ecocardiographic applications. Under a common Principal Component Analysis, we then evaluated left ventricle morphological changes (at both layers) explained by first Principal Component scores. Trajectories’ shape and orientation were investigated and contrasted. Logistic regression and Receiver Operating Characteristic curves were used to compare these morphometric indicators with traditional 3D Speckle Tracking Echocardiography global parameters. Geometric morphometrics indicators performed better than 3D Speckle Tracking Echocardiography global parameters in recognizing pathology both in systole and diastole. Genotype positive/phenotype negative individuals clustered with patients affected by hypertrophic cardiomyopathy during diastole, suggesting that incumbent pathology may indeed be foreseen by these methods. Left ventricle deformation in patients affected by hypertrophic cardiomyopathy compared to healthy subjects may be assessed by modern shape analysis better than by traditional 3D Speckle Tracking Echocardiography global parameters. Hypertrophic cardiomyopathy pathophysiology was unveiled in a new manner whereby also diastolic phase abnormalities are evident which is more difficult to investigate by traditional ecocardiographic techniques.

A common age effect model for the mortality of multiple populations

We introduce a model for the mortality rates of multiple populations. To build the proposed model we investigate to what extent a common age effect can be found among the mortality experiences of several countries and use a common principal component analysis to estimate a common age effect in an age–period model for multiple populations. The fit of the proposed model is then compared to age–period models fitted to each country individually, and to the fit of the model proposed by Li and Lee (2005).Although we do not consider stochastic mortality projections in this paper, we argue that the proposed common age effect model can be extended to a stochastic mortality model for multiple populations, which allows to generate mortality scenarios simultaneously for all considered populations. This is particularly relevant when mortality derivatives are used to hedge the longevity risk in an annuity portfolio as this often means that the underlying population for the derivatives is not the same as the population in the annuity portfolio.