Nonlinear Principal Component Analysis Research Articles

Traumatic and Adverse Childhood Experiences and Developmental Differences in Psychiatric Risk

While adverse childhood experiences (ACEs) are known to impart significant risk for negative mental health and cognitive outcomes in youth, translation of ACE scores into clinical intervention is limited by poor specificity in predicting negative outcomes. This work expands on the ACE framework using a data-driven approach to identify 8 different forms of traumatic and adverse childhood experiences (TRACEs) and reveal their differential associations with psychiatric risk and cognition across development. Building upon the traditional ACEs model, this study aimed to characterize unique components of commonly co-occurring TRACEs and to examine moderation of longitudinal change in mental health and cognitive development during adolescence. This work draws from youth and their caregivers who completed up to 4 annual behavioral assessments from 2016 to 2021 as part of the ongoing Adolescent Brain Cognitive Development (ABCD) study. Data collection was performed at 21 regionally-distributed sites across the United States. Analyses for this work were conducted January 2023 through November 2023. Youth participants in the ABCD study's exposure to 268 different TRACEs, which were distilled into adversity components using nonlinear principal components analysis. Mixed-effects and latent change score models considered TRACEs components as moderators of longitudinal change in internalizing and externalizing mental health problems, as well as longitudinal change in cognitive ability. Data were distilled from 11 876 youth participants, who were grouped into dyads with a caregiver. ABCD study youth participants were 9 to 10 years old at baseline assessment (year 0) and 12 to 13 years old at ABCD year 3. A total of 5679 participants (47.8%) were female. Analyses revealed that TRACEs organized into 8 thematic adversity components (e.g., family conflict, interpersonal violence). At baseline assessment (year 0), exposure to nearly every adversity component was associated with poorer mental health and diminished cognitive ability. Yet across time, it was observed that different forms of adversity were variably linked to both increases and decreases in internalizing and externalizing problems. For example, while peer aggression (t = 5.31) and family conflict (t = 5.67) were associated with increases in both internalizing and externalizing problems over early adolescence, community threat (t = 2.82) and poverty (t = 2.07) were linked to decreased problems, potentially representing adaptive suppression of symptoms. Finally, adversity types related to resource deprivation (eg, poverty, caregiver maladjustment) were associated with declines in cognitive ability over early adolescence. In this cohort study, distinct forms of TRACEs differentially moderated developmental changes in psychiatric risk and cognitive ability in different ways, offering the possibility for precision-based prediction of risk for youth. Such findings could be used in targeted early prevention and intervention strategies for at-risk youth.

Нелинейные главные компоненты биохимического профиля молочного скота пяти пород

Abstract. The purpose of this study was to identify and explain patterns of biochemical parameters in dairy cows, taking into account the characteristics of their breed. Methods. A comparison was made of samples of animals of five breeds (Holstein, Tagil, Suksun, Sychevsk, Istoben) according to 17 biochemical parameters. The total number of animals studied was 407. To analyze the obtained results, statistical analysis methods were used, including the method of nonlinear principal component analysis using the CATPCA (Categorical PCA) algorithm. Scientific novelty. The chosen method made it possible to summarize a large number of biochemical indicators and identify processes in which the studied groups of cows differed to a greater or lesser extent. Results. Five principal components were identified and interpreted, explaining a total of 67.4 % of the total variance. Some observed patterns may indicate the development of pathological conditions in animals. Similar biochemical patterns were observed, on the one hand, in cows of the Tagil, Holstein and Suksun breeds, and on the other, in the Istoben and Sychev breeds. The animals of the Suksun breed were closest to the physiological norm. More pronounced changes associated with a negative energy balance were observed in Holstein cows.

Examination of the effect of ANN and NLPCA technique on prediction performance in patients with breast tumors

Breast cancer is among the most prevalent cancer kinds worldwide. The aim of this study is to examine the effect of combining Artificial Neural Networks and Nonlinear Principal Component Analysis techniques on prediction performance in patients with breast tumors. In the application, a network containing 5 layers, including the input, the coding, the bottleneck, the decoding and the output, was used for the 30 variable data set of 569 breast tumor patients. The training algorithm of choice was the Conjugate Gradient Descent (CGD) algorithm. In this study, artificial neural networks (ANN) and nonlinear principal component analysis were coupled. NLPCA was first applied to dimension reduction in artificial neural networks. Using both the original data set and the decreased size, artificial neural networks were used in the second stage to develop prediction models. By contrasting the performance of these two prediction models with one another, the outcomes were understood. 96.37% of the variation was explained by the two fundamental components that were found using NLPCA. The prediction models developed for the original data set and the dimension-reduced data set have R2 values of 91% and 87%, respectively. The advantages of the NLPCA and ANN combination for breast tumor patients are demonstrated by this study. It is believed that utilizing principal components as inputs can cut down on complexity and extraneous information.

Nonlinear Principal Component Analysis of Food Hygiene and Safety Knowledge and Practices Among Street Food Vendors

Nonlinear Principal Component Analysis of Food Hygiene and Safety Knowledge and Practices Among Street Food Vendors

Exploring the Botanical Origins of Bee-Collected Pollen: A Comprehensive Historical and Contemporary Analysis.

Bee-collected pollen is one of the most important bee products. In order to predict its chemical composition and nutritional value botanical origin of pollen plays a crucial role. This review intended to collect all available data published about botanical origin of pollen collected all around the world. Due to enourmous amount of data and variables nonlinear principal component analysis (NLPCA), by applying Categorical Principal Component Analysis (CATPCA), was conducted in order to try to determine any specifity and/or differences among samples. Also, importance of some plant families/genera/species for bees was monitored. Based on CATPCA results families can serve in order to distinct samples from North/South America. Also, some samples from Europe (Turkey and Serbia) were characterized with presence of some specific families. Genera were excellent tool to distinguish samples from different parts of Brazil as well as Australia, Asia and Africa. Due to high and specific biodiversity pollen samples obtained from Sonoran desert (USA) completely were separated during analysis. This review presents the first attempt to summarize and classify a large number of data about botanical sources of bee-collected pollen.

A Comparison of the Smile Esthetic Understanding of Periodontists, Orthodontists, General Dentists, and Dental Students

Objective: The aim of this study was to evaluate smile esthetics among periodontists, orthodontists, general dentists, and dental students. Methods: The study consisted of five groups: periodontists, orthodontists, general dentists, fifth-year dental students, and fourth-year dental students. The evulaters used the Smile Esthetics Index (SEI) consisting of 10 items to analyze the natural smile photographs of 15 different individuals. One-way ANOVA was performed for the comparison of group means. The Duncan multiple comparison test was also used to identify different groups. Non-linear principal component analysis was performed to determine the configuration of the relationship between categories of variables in two-dimensional space. Results: The group with the highest reliability coefficient among the groups was the fourth-year dental students (Cronbach's Alpha = 0.89). This was followed by general dentists, periodontists, fifth-year dental students, and orthodontists. According to the evaluation of the total score averages of the fifteen pictures, a statistically significant difference was observed between the groups (p = 0.041). Accordingly, the fourth-year dental students (5.78 ±1 .13) had a lower total score than the fifth-year dental students (6.56 ± 0.88), and this difference was statistically significant. However, no significant difference was observed between the fourth-year dental students and the general and specialized dentists and between the fifth-year dental students and graduated and specialized dentists in terms of the total score given to the pictures. Conclusion: It was observed that the reliability coefficient was high in all groups evaluated with SEI. It can be concluded that esthetic perception is formed by actively performing the profession of dentistry, and this situation does not change according to specialization.

Seasonal influence of tropical Pacific and Atlantic sea surface temperature on streamflow variability in the patia river basin

This research presents a seasonal analysis of the variability of streamflows in the Patía River Basin (PRB) between 1984 and 2018 and the influence exerted by the large-scale climate variability using non-linear principal component analysis (NLPCA), Pearson's correlation, and composite analysis. The study was conduced during the minimum (July–August–September, JAS) and maximum (October–November–December, OND) streamflow periods. The NLPCA depicted a single significant mode of variability for each season with explained variances greater than 75%. The correlation analysis between the main mode of variability during OND and climate indices showed significant results, mainly with the Pacific Ocean and El Niño-Southern Oscillation (ENSO). In contrast, for JAS, the correlations were significant for the indices linked to the Atlantic Ocean. Finally, the composite analysis indicated that the positive (negative) events during JAS, which show the increase (decrease) of streamflow in PRB, are related to negative (positive) anomalies in the Tropical Northern Atlantic band, including the Caribbean Sea and the Gulf of Mexico. In comparison, the positive (negative) events during OND are related to negative (positive) sea surface temperature (SST) anomalies in the tropical Pacific, corresponding to La Niña (El Niño) events. The results provide evidence of the strong influence of climate indices and tropical Pacific and Atlantic SST on seasonal streamflow in the PRB and establish the foundations for seasonal streamflow modelling, relevant for prevention and risk management as well as for adequate planning and management of water resources in the region.

NONLINEAR PRINCIPAL COMPONENT ANALYSIS IN PATH ANALYSIS WITH LATENT VARIABLES MIXED DATA

This study aims to obtain the main component score of the ability to pay latent variable, determine the strongest indicators forming the ability to pay on a mixed scale based on defined indicators, and model the ability to pay on time mediated by fear of paying using path analysis. The data used in this study is secondary data from mortgage-paying customers with a sample size of 100. The method used is nonlinear principal component analysis with path analysis modeling. The results of this study indicate that the eleven variables formed by PC1 or X1 are able to store diversity or information by 32.50%, while 67.50% of diversity or other information is not stored (wasted). The credit term is the strongest indicator that forms the ability to pay variable. The variable ability to pay mortgages has a significant effect on payments by mediating the fear of paying late with a coefficient of determination of 80.40%.

THE ROLE OF AGRICULTURAL SUBSIDIES IN SHAPING YOUNG FARMERS' DECISION-MAKING IN THE GREEK AGRICULTURAL SECTOR

The Greek agricultural sector is grappling with a dynamic environment characterized by evolving policies, global market forces, and reduced protectionism. To effectively navigate these challenges, a paradigm shift toward a new development philosophy is imperative. This philosophy seeks to address structural issues, enhance technical and economic aspects, and strategically orient production towards crops that capitalize on the Mediterranean region's comparative advantages. This study explores Greek farmers' attitudes, expectations, and satisfaction levels in this evolving landscape, with a particular emphasis on assessing the impact of the Young Farmers 2021 program and demographic factors. Utilizing a questionnaire of 18 closed-ended questions, data were collected from 144 respondents, comprising 76 participants in the Young Farmers 2021 program and 68 non-participants. The survey encompassed inquiries into demographic and social variables and delved into the assessment of attitudes, expectations, and satisfaction levels. The collected data underwent rigorous analysis involving Non-Linear Principal Component Analysis and categorical regression. The examination yielded two discernible factor axes: „Challenges and Diminished Satisfaction“ and „Favorable Perceptions and Optimistic Prospects.“ Remarkably, Young Farmers 2021 program participants exhibited fewer hurdles, higher contentment, and a more sanguine outlook compared to non-participants. These disparities were statistically significant, underscoring the program's substantial influence. Furthermore, demographic attributes emerged as pivotal determinants. A higher level of educational attainment was correlated with diminished difficulties, augmented satisfaction, and more favorable perceptions and expectations. This underscores the pivotal role of educational initiatives in equipping farmers with the necessary acumen and skills to effectively surmount obstacles and cultivate optimism within the agricultural sector. In summary, this study underscores the noteworthy impact of the Young Farmers 2021 program on Greek farmers' attitudes, expectations, and contentment levels. Program involvement was associated with reduced challenges, heightened satisfaction, and a more positive outlook regarding the future of agriculture. Additionally, educational attainment surfaced as a pivotal factor in molding farmers' experiences and outlooks. These insights offer valuable guidance to policymakers and agricultural organizations, facilitating the development of targeted interventions and policies aimed at encouraging program participation and delivering educational resources. Keywords: agricultural subsidies, decision-making, financial considerations, policymaking. JEL: Q12; Q14; O2

Nonlinear Principal Component Analysis with Mixed Data Formative Indicator Models in Path Analysis

This research aims to obtain the main component score of the latent variable ability to pay, determine the strongest indicators forming the ability to pay on a mixed scale based on predetermined indicators, and model the ability to pay on time as mediated by fear of paying using path analysis. The data used is secondary data obtained through distributing questionnaires with a mixed data scale. The sampling technique used in the research was purposive sampling. The number of samples used in the research was 100 customers. The method used is nonlinear principal component analysis with path analysis modeling. The results of this research show that of the five indicators formed by the Principal Component, 74.8% of diversity or information is able to be stored, while 25.20% of diversity or other information is not stored (wasted). Credit term is the strongest indicator that forms the ability to pay variable. The variable ability to pay mortgage has a significant effect on payments by mediating the fear of being late in paying with a coefficient of determination of 73.63%.

Structure-Preserving Non-Linear PCA for Matrices

Structure-Preserving Non-Linear PCA for Matrices

Applications of Rasch modeling in chemometrics: Binary data analysis and analytical platform selection

Often researchers make use of Principal Component Analysis and Partial Least Squares Regression, an unsupervised and a supervised method, respectively, to extract the chemical information in the shape of one or more latent variables. However, when the research question is qualitative and requires a figure of merit, these two models will primarily focus on the quantitative and continuous information present in the data. In these cases, a valid approach may be to dichotomize the data and to analyze the resulting non-linear data via Non-Linear Principal Component Analysis. However, the results of the method are not always easy to interpret due to the possible multidimensionality of the solution. Here we introduce an alternative framework, composed of Rasch modeling and Generalized Linear Mixed Effect Models, to extract information from multivariate binary chemical data with an underlying design of experiment. The model obtained by this framework provides information in a unidimensional representation that can be easily translated into one-dimensional action and control. Furthermore, we show that, through Generalized Linear Mixed Effect Models, it is possible to extend the Rasch model to its multilevel form, which enables the consideration of each random factor possibly present.

Variability, cycles, and trends of mean air temperature north of Colombia

Climate variability is of global interest due to its socioeconomic and environmental effects on the world’s population. In Colombia, temperature changes affect food security, especially for the most vulnerable people in the Caribbean region. We analyzed monthly air temperature in northeastern Colombia (Cesar, La Guajira, and Magdalena departments). We reconstructed time series with missing data using nonlinear principal component analysis. Subsequently, temporal variability, associations with events of climatic variability, and temporal trends were evaluated. Periodicity analyses indicate the dominance of annual variability, although statistically significant associations with periods between 3 and 7 years show the influence of El Niño-Southern Oscillation (ENSO) events. The Spearman correlation coefficient with N = 360 and 95% significance shows a better association with the Multivariate ENSO Index (rsp mean = 0.38) and the Southern Oscillation Index (rsp mean = –0.32). The multi-year monthly analysis shows positive trends, with maximum values between March (1.04 ºC month–1), and June (1.07 ºC month–1) in the valley of the Cesar department, and a minimum in March, at the northernmost La Guajira (0.2 ºC month–1).

Deep Kernel Principal Component Analysis for multi-level feature learning

Principal Component Analysis (PCA) and its nonlinear extension Kernel PCA (KPCA) are widely used across science and industry for data analysis and dimensionality reduction. Modern deep learning tools have achieved great empirical success, but a framework for deep principal component analysis is still lacking. Here we develop a deep kernel PCA methodology (DKPCA) to extract multiple levels of the most informative components of the data. Our scheme can effectively identify new hierarchical variables, called deep principal components, capturing the main characteristics of high-dimensional data through a simple and interpretable numerical optimization. We couple the principal components of multiple KPCA levels, theoretically showing that DKPCA creates both forward and backward dependency across levels, which has not been explored in kernel methods and yet is crucial to extract more informative features. Various experimental evaluations on multiple data types show that DKPCA finds more efficient and disentangled representations with higher explained variance in fewer principal components, compared to the shallow KPCA. We demonstrate that our method allows for effective hierarchical data exploration, with the ability to separate the key generative factors of the input data both for large datasets and when few training samples are available. Overall, DKPCA can facilitate the extraction of useful patterns from high-dimensional data by learning more informative features organized in different levels, giving diversified aspects to explore the variation factors in the data, while maintaining a simple mathematical formulation.

Multidimensional investigation of the effect of pre-treatment solutions on drying characteristics and raisin quality

This study aims to evaluate the relationships between pre-treatment solution, variety, drying characteristics, and raisin quality in raisin production, using multivariate analysis methods. This study was conducted on raisins obtained by dipping Bineteti and Zeyti local seed grape varieties in 13 different pre-treatment solutions and drying in the sun on a concrete drying platform. In the study, data of 15 numerical variables related to drying characteristics and raisin quality were reduced to four principal components (PC1, PC2, PC3 and PC4) by principal component analysis (PCA), and their score values were numerically obtained. Then, two grape varieties, 13 pre-treatment solutions, and the four principal components were analyzed by non-linear principal component analysis (NLPCA). In addition, cluster analysis was performed to determine the prominent pre-treatment solutions in terms of drying characteristics and raisin quality. It was determined that the pre-treatment solutions were effective on L*, a*, b*, chroma (C*), hue (h°), a/b values, antioxidant activity, total phenolic content, and drying time constituting PC1. It was remarkable that the colour parameters in prominent clusters in cluster analysis also form PC1 in PCA analysis. The best pre-treatment solutions were found to be the "5% K2CO3 + 1% olive oil" solution for the Bineteti variety and the "5% K2CO3 + 2% hazelnut oil" solution for the Zeyti variety. It was determined that the pre-treatment solutions recommended for the varieties caused to be obtained brighter, more vibrant, and yellow-coloured raisins and shortened the drying time, and had positive effects on the total phenolic content and antioxidant activity.

How to measure banking regulation and supervision

This paper uses data from 141 countries to identify the variables that best characterize worldwide banking regulation and supervision practices. We apply a nonlinear principal components analysis with optimal variable transformation to deal with the variables’ mixed measurement levels and reduce data dimensionality. The robustness of the results is tested for different subsamples. The findings indicate that deposit insurance, liquidity, diversification requirements, complementary banking activities, and market discipline are the most reliable indicators to measure regulation. In contrast, resolution activities, the mandate of the head of the supervisory agency, and the report of prudential regulation infractions assume the same role for banking supervision. Capital requirements and ownership are of minor relevance and are sensitive to a country’s development level. China and Germany display the most distinct regulation practices, while China and the UK adopt the most stringent policies regarding supervision.

Machine learning methods for nonlinear dimensionality reduction of the thermospheric density field

Accurate prediction of the thermospheric density field has recently been gaining a lot of attention, due to an outstanding increase in space operations in Low-Earth Orbit, in the context of the NewSpace. In order to model such high-dimensional, non-Gaussian systems, Reduced-Order Models (ROMs) have been developed against existing physics-based density models. In general, the data-driven reconstruction of dynamical systems usually consists of two steps of compression and prediction. In this paper, we focus on the compression step and assess state-of-the-art order reduction methodologies such as autoencoders, linear, and nonlinear Principal Component Analysis (PCA). We show that Kernel-based PCA, a nonlinear generalization of PCA, can perform better than Neural Networks in representing the model in low-dimensional space in terms of accuracy, computational time, and energy consumption for both the Lorenz system, a chaotic dynamical system developed in the context of atmospheric modeling, and the thermospheric density.

Dimensionality study of ground motion spectra through nonlinear principal component analysis

Dimensionality study of ground motion spectra through nonlinear principal component analysis

Estimation of the desert dust balance and its relationship with environmental factors in the southern Baja California Peninsula

The emission, transport, and deposition of desert dust are influenced by environmental factors evaluated mainly as a function of precipitation, air temperature, and wind. The present study estimates the desert dust balance and its relationship with environmental factors for the period 1981–2020 in the Baja California Sur region, Mexico. Monthly data on wind, air temperature, precipitation, dust emission and deposition from in situ measurements, reanalysis, satellite estimates and a numerical parameterization scheme are used. First, the in-situ time series were reconstructed with a non-linear principal component analysis based on an autoassociative neural network. We then evaluated the performance of the satellite estimates and the reanalysis data using the matching technique, after which we quantified the dust emission in three source zones. To estimate the state of the climate, we obtained the annual cycle of monthly climatologies, and yearly averages. The effect of climate on the dust budget is estimated by means of non-parametric associations between the variables of interest. Finally, we estimate climate trends using Pettitt, Modified Man-Kendall and Theil-Sen tests. The results show that the data sets used represent the mean and variability of in situ measurements. There are three seasons with transitional periods for precipitation and air temperature. The wind shows a longitudinal west–east gradient. Its maximum intensity is from April to September. Northern zone was dominated by highest emissions. Dry deposition mechanism was the most representative. Emissions and dry deposition are controlled by wind intensity and precipitation, while precipitation controls wet deposition. The Pettitt test showed that abrupt changes in the time series coincide with periods where positive sunspot anomalies converge with negative PDO and SOI anomalies. Finally, trend analyses identified a temporal broadening of the dry season, warmer winters, and summers with increases of ~ 0.02 °C, predominantly increasing trends in dust emissions and wind intensity, and a progressive decrease in dust deposition mechanisms.

Confidentiality-preserving machine learning algorithms for soft-failure detection in optical communication networks

Automated fault management is at the forefront of next-generation optical communication networks. The increase in complexity of modern networks has triggered the need for programmable and software-driven architectures to support the operation of agile and self-managed systems. In these scenarios, the European Telecommunications Standards Institute zero-touch network and service management approach is imperative. The need for machine learning algorithms to process the large volume of telemetry data brings safety concerns as distributed cloud-computing solutions become the preferred approach for deploying reliable communication network automation. This paper’s contribution is twofold. First, we propose a simple yet effective method to guarantee the confidentiality of the telemetry data based on feature scrambling. The method allows the operation of third-party computational services without direct access to the full content of the collected data. Additionally, the effectiveness of four unsupervised machine learning algorithms for soft-failure detection is evaluated when applied to the scrambled telemetry data. The methods are based on factor analysis, principal component analysis, nonlinear principal component analysis, and singular value decomposition. Most dimensionality reduction algorithms have the common property that they can maintain similar levels of fault classification performance while hiding the data structure from unauthorized access. Evaluations of the proposed algorithms demonstrate this capability.