Principal components analysis (PCA) has long been used to decompose the ERP into components, and these mathematical entities are increasingly accepted as meaningful and useful representatives of the ...electrophysiological components constituting the ERP. A similar expansion appears to be beginning in regard to decomposition of the EEG amplitude spectrum into frequency components via frequency PCA. However, to date, there has been no exploration of the brain's dynamic EEG‐ERP linkages using PCA decomposition to assess components in each measure. Here, we recorded intrinsic EEG in both eyes‐closed and eyes‐open resting conditions, followed by an equiprobable go/no‐go task. Frequency PCA of the EEG, including the nontask resting and within‐task prestimulus periods, found seven frequency components within the delta to beta range. These differentially predicted PCA‐derived go and no‐go N1 and P3 ERP components. This demonstration suggests that it may be beneficial in future brain dynamics studies to implement PCA for the derivation of data‐driven components from both the ERP and EEG.
Despite several decades of study, ambiguities persist in terms used to express environmental and biotic occurrences of polychlorinated alkanes (PCAs), the main ingredient of chlorinated paraffins ...(CPs). This can lead to misinterpretation of data between analytical chemists, toxicologists, risk assessors/managers and regulators. The terms recommended here to harmonise reporting and reduce ambiguity use the conventional definition of PCAs linear chlorinated alkanes (typically, C->= 10) with one chlorine per carbon, although some evidence of multiple chlorination exists. Other recommendations include.reporting the "Sum of measured PCAs" because "Total PCAs" is currently unquantifiable.reporting individual chain lengths, e.g., Sigma PCAs-C-11, Sigma PCAs-C-13, allows easier comparability and allows toxicology and risk assessment to consider different PCA combinations.maintain studies on individual PCAs in order to better characterise chemical, environmental and health risk behaviour.The terms could be extended in future to assimilate new findings on individual PCAs, multiple chlorination and chirality.
•In this work, we propose a new data visualization and clustering technique for discovering discriminative structures in high-dimensional data.•This technique, referred to as cPCA++, is motivated by ...the fact that the interesting features of a “target” dataset may be obscured by high variance components during traditional PCA.•By analyzing what is referred to as a “background” dataset (i.e., one that exhibits the high variance principal components but not the interesting structures), our technique is capable of efficiently highlighting the structures that are unique to the “target” dataset.•Similar to another recently proposed algorithm called “contrastive PCA” (cPCA), the proposed cPCA++ method identifies important dataset-specific patterns that are not detected by traditional PCA in a wide variety of settings.•However, unlike cPCA, the proposed cPCA++ method does not require a parameter sweep, and as a result, it is significantly more efficient.
In this work, we propose a new data visualization and clustering technique for discovering discriminative structures in high-dimensional data. This technique, referred to as cPCA++, is motivated by the fact that the interesting features of a “target” dataset may be obscured by high variance components during traditional PCA. By analyzing what is referred to as a “background” dataset (i.e., one that exhibits the high variance principal components but not the interesting structures), our technique is capable of efficiently highlighting the structures that are unique to the “target” dataset. Similar to another recently proposed algorithm called “contrastive PCA” (cPCA), the proposed cPCA++ method identifies important dataset-specific patterns that are not detected by traditional PCA in a wide variety of settings. However, unlike cPCA, the proposed cPCA++ method does not require a parameter sweep, and as a result, it is significantly more efficient. Several experiments were conducted in order to compare the proposed method to state-of-the-art methods. These experiments show that the proposed method achieves performance that is similar to or better than that of the other methods, while being more efficient.
For industrial chemical process, preliminary-summation-based principal component analysis (PS-PCA), an amended PCA method was recently provided for coping with both Gaussian and non-Gaussian ...characteristics. By summing the training and monitoring data respectively, PS-PCA is capable of resolving the issue of non-Gaussian processes and achieves higher fault detection rate than the traditional PCA. However, in the PS-PCA summation operation, all data samples are regarded as the same weight, which results in the fault information of newly-samples may be diluted, leading to significant detection delays. To address this challenge, in this paper, we propose a novel weighted PS-PCA (WPS-PCA) method that employs an exponential weighting scheme to put more emphasis on recent information. Subsequently, a mathematical argument demonstrates that when the number of variables is enough plentiful, the obtained summation combined with the generalized central limit theorem conforms to approximately a Gaussian distribution. The kurtosis relationships indicate this conversion will bring out well-pleasing feasibility for conventional PCA. Ultimately, the proposed technique verifies detection performance using the Tennessee Eastman process, which is compared with the existing PCA and PS-PCA schemes, in terms of the fault detection time and fault detection rate. The simulation studies reveal that the proposed method is efficient and superior.
Prostate cancer (PCa) kills one man in forty five and represents the most frequently diagnosed cancer in American men. As is true with cancer, early detection through digital rectal exam and serum ...testing for prostate specific antigen have greatly reduced the number of fatalities yet these same methods sometimes lead to overdiagnosis and overtreatment. Previous studies have highlighted the association between single nucleotide polymorphisms (SNPs) and cancer. In our studies, we sought to find correlations between SNPs on chromosome 8 and PCa. Normal and PCa patient sequencing data was obtained from the Sequence Read Archive and then analysis pipelines were constructed to map the sequences against chromosome 8 followed by indexing and variant calling. Our studies were able to identify four SNPs specific to DNA sequences isolated from prostate cancer tissues but that were absent in normal tissues. We hope that our findings might help improve personalized medicine and possibly reduce the incidences of overdiagnosis and overtreatment of PCa.
Facing with rapidly increasing demands for analyzing high-order data or multiway data, feature-extracting methods become imperative for analysis and processing. The traditional feature-extracting ...methods, however, either need to overly vectorize the data and smash the original structure hidden in data, such as PCA and PCA-like methods, which is unfavorable to the data recovery, or cannot eliminate the redundant information very well, such as tucker decomposition (TD) and TD-like methods. To overcome these limitations, we propose a more flexible and more powerful tool, called the multiview principal components analysis (Multiview-PCA) in this article. By segmenting a random tensor into equal-sized subarrays called sections and maximizing variations caused by orthogonal projections of these sections , the Multiview-PCA finds principal components in a parsimonious and flexible way. In so doing, two new operations on tensors, the <inline-formula> <tex-math notation="LaTeX">S </tex-math></inline-formula>- direction inner/outer product , are introduced to formulate tensor projection and recovery. With different segmentation ways characterized by section depth and direction , the Multiview-PCA can be implemented many times in different ways, which defines the sequential and global Multiview-PCA, respectively. These multiple Multiview-PCA take the PCA and PCA-like, and TD and TD-like as the special cases, which correspond to the deepest section depth and the shallowest section depth, respectively. We propose an adaptive depth and direction selection algorithm for the implementation of Multiview-PCA. The Multiview-PCA is then tested in terms of subspace recovery ability, compression ability, and feature extraction performance when applied to a set of artificial data, surveillance videos, and hyperspectral imaging data. All numerical results support the flexibility, effectiveness, and usefulness of Multiview-PCA.
Display omitted
•PCA-based quantitation of T1 relaxation data outperforms integration in general.•The actual benefit of using PCA depends on the signal shape and quantitation domain.•PCA-based ...quantitation in the time domain can be performed on full-length FIDs.•Data correction for frequency or phase variations, if any, is a prerequisite for PCA.
Principal component analysis (PCA) has proved to be a powerful technique for processing NMR data. It is particularly useful in signal quantitation where it often provides better results compared to a direct integration of individual signals. In the present work, we recapitulate the principles and theoretical framework underlying PCA-based quantitation with a special focus on T1 relaxometry. We show that under commonly encountered conditions, this approach can provide up to ~4-fold improvement in scatter of points in magnetization build-up curves compared to direct integration. Best practices to optimize the PCA performance in measuring the total magnetization are discussed, including minimization of the number of signal-related principal components and a proper selection of FT parameters and data quantitation intervals. For signals consisting of distinct relaxation components, formulas are provided for resolving the components relaxation and illustrated on a real-data example. In addition to the problem of quantitation, the use of PCA in denoising of partially relaxed spectra is discussed in connection with such applications as line shape analysis and monitoring relaxation of individual spectral components.
•This paper studies the fault detection of nonlinear system using kernel method.•An online monitoring method for extracting the reduced number of measurements from the training data was proposed.•To ...evaluate the performance of the proposed method is applied for monitoring a Tennessee Eastman Process.
Kernel principal component analysis (KPCA), which is a nonlinear extension of principal component analysis (PCA), has gained significant attention as a monitoring method for nonlinear processes. However, KPCA cannot perform well for dynamic systems and when the training data set is large. Therefore, in this paper, an online reduced KPCA algorithm for process monitoring is proposed. The process monitoring performances are studied using two examples: a numerical example and Tennessee Eastman Process (TEP). The simulation results demonstrate the effectiveness of the proposed method when compared to the online KPCA method.
The combustion of fuels in industries is one of the sources of greenhouse gases, posing a considerable threat to human health and the natural environment. The monitoring of the combustion process and ...the measurement of combustion degrees, therefore, are crucial and significant. In this work, an experimental system was developed based on Laser-induced breakdown spectroscopy (LIBS). Charcoals with different combustion degrees were taken as samples, and in the LIBS spectra of charcoals, the characteristic line of C and the molecular bands of CN and CaO were observed. Moreover, a univariate calibration model based on the exponential fitting curve was constructed to predict the combustion degree according to the variation of the C I line intensity under different combustion degrees. Furthermore, different algorithms including support vector machine (SVM), partial least squares (PLS), random forest (RF), and convolutional neural network (CNN) were applied to establish the multivariate calibration model, and the prediction performance was compared according to the 10-fold cross-validation. Particle swarm optimization (PSO) was used to optimize hyperparameters of the selected SVM-based multivariate calibration model. Principal component analysis (PCA) and linear discriminant analysis (LDA) were employed to extract spectral features and reduce dimensions for the improvement of the model's performance, with the final root mean square error of prediction (RMSEP) and mean relative error (MRE) of the LDA-PSO-SVM calibration model of 0.035 and 3.877%, respectively. Finally, c. The results prove feasible to realize the real-time determination of combustion degree to control carbon emissions and monitor carbon concentration through the LIBS-based method with machine learning, which also provides new ideas for the application of LIBS in the atmospheric field.
Display omitted
•A novel real-time detection method for combustion degree was developed based on LIBS and machine learning.•An exponential variation trend of C Ⅰ 247.86 nm intensity was obtained as a function of combustion degrees.•Different algorithms were used to establish the calibration model to predict the combustion degree.•PSO was used to optimize hyperparameters of the model, and LDA and PCA were employed to extract spectral features.
La utilización de nuevas tecnologías asociadas a la agricultura de precisión permite capturar información de múltiples variables en gran cantidad de sitios georreferenciados dentro de lotes en ...producción. Las covariaciones espaciales de las propiedades del suelo y el rendimiento del cultivo pueden evaluarse a través del análisis de componentes principales clásico (PCA). No obstante, como otros métodos multivariados descriptivos, el PCA no ha sido desarrollado explícitamente para datos espaciales. Nuevas versiones de análisis multivariado permiten contemplar la autocorrelación espacial entre datos de sitios vecinos. En este trabajo se aplican y comparan los resultados de dos técnicas multivariadas, el PCA y MULTISPATI-PCA. Este último incorpora la información espacial a través del cálculo del índice de Moran entre los datos de un sitio y el dato promedio de sus vecinos. Los resultados mostraron que utilizando MULTISPATI-PCA se detectaron correlaciones entre variables que no fueron detectadas con el PCA. Los mapas de variabilidad espacial construidos a partir de la primera componente de ambas técnicas fueron similares; no así los de la segunda componente debido a cambios en la estructura de co-variación identificada, al corregir la variabilidad por la autocorrelación espacial de los datos. El método MULTISPATI-PCA constituye una herramienta importante para el mapeo de la variabilidad espacial y la identificación de zonas homogéneas dentro de lotes.
PDF
