Classical Principal Component Analysis Research Articles

A Convolution Auto-Encoders Network for Aero-Engine Hot Jet FT-IR Spectrum Feature Extraction and Classification

Aiming at classification and recognition of aero-engines, two telemetry Fourier transform infrared (FT-IR) spectrometers are utilized to measure the infrared spectrum of the areo-engine hot jet, meanwhile a spectrum dataset of six types of areo-engines is established. In this paper, a convolutional autoencoder (CAE) is designed for spectral feature extraction and classification, which is composed of coding network, decoding network, and classification network. The encoder network consists of convolutional layers and maximum pooling layers, the decoder network consists of up-sampling layers and deconvolution layers, and the classification network consists of a flattened layer and a dense layer. In the experiment, data for the spectral dataset were randomly sampled at a ratio of 8:1:1 to produce the training set, validation set, and prediction set, and the performance measures were accuracy, precision, recall, confusion matrix, F1 score, ROC curve, and AUC value. The experimental result of CAE reached 96% accuracy and the prediction running time was 1.57 s. Compared with the classical PCA feature extraction and SVM, XGBoost, AdaBoost, and Random Forest classifier algorithms, as well as AE, CSAE, and CVAE deep learning classification methods, the CAE network can achieve higher accuracy and efficiency and can complete the spectral classification task.

Deep Learning Based Atrial Fibrillation Detection Using Combination of Dimensionality Reduction Techniques and RR Interval Features

Purpose: Accurate detection of Atrial Fibrillation (AF) has great significance in the field of medical science which can reduce the rate of mortality and morbidity. The present study focuses on Electrocardiography (ECG) signal classification using dimensionality reduction techniques combined with R wave to R wave interval (RR interval) features. Materials and Methods: In the first approach, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA), and Probabilistic Principal Component Analysis (PPCA) are performed independently on denoised ECG signal using Discrete Wavelet Transform (DWT) for the classification of ECG signal. In the second approach, the dimensionality reduction techniques combined with RR interval features are used for the classification of ECG signal. Results: Machine Learning (ML) algorithms such as Decision Tree (DT), Support Vector Machine (SVM), and Deep Learning (DL) algorithms such as Long Short Term Memory (LSTM) and Bi-Directional LSTM (BiLSTM) are used for classification purposes. Conclusion: The proposed methodology provided an overall accuracy of 93.65% with PCA and LSTM classifier and an overall accuracy of 99.45% with PCA combined with RR interval features and LSTM classifier. The developed technology has potential applications in many practical solutions.

Enhanced Pansharpening Using Curvelet Transform Optimized by Multi Population Based Differential Evolution

In this study, a pansharpening process was conducted to merge the color information of low-resolution RGB images with the details of high-resolution panchromatic images to obtain higher quality images. During this process, weight optimization was performed using the Curvelet Transform method and the Multi Population Based Differential Evolution (MDE) algorithm. The proposed method was tested on Landsat ETM satellite image. For Landsat ETM data, the RGB images have a resolution of 30m, while the panchromatic images have a resolution of 15m. To evaluate the performance of the study, the proposed MDE-optimized Curvelet Transform-based pansharpening method was compared with classical IHS, Brovey, PCA, Gram-Schmidt and Simple Mean methods. The comparison process employed metrics such as RMSE, SAM, COC, RASE, QAVE, SID, and ERGAS. The results indicate that the proposed method outperforms classical methods in terms of both visual quality and numerical accuracy.

Nonlinear Reduced Order Modelling for Korteweg-de Vries Equation

Efficient computational techniques that maintain the accuracy and invariant preservation property of the Korteweg-de Vries (KdV) equations have been studied by a wide range of researchers. In this paper, we introduce a reduced order model technique utilizing kernel principle component analysis (KPCA), a nonlinear version of the classical principle component analysis, in a non-intrusive way. The KPCA is applied to the data matrix, which is formed by the discrete solution vectors of KdV equation. In order to obtain the discrete solutions, the finite differences are used for spatial discretization, and linearly implicit Kahan's method for the temporal one. The back-mapping from the reduced dimensional space, is handled by a non-iterative formula based on the idea of multidimensional scaling (MDS) method. Through KPCA, we illustrate that the reduced order approximations conserve the invariants, i.e., Hamiltonian, momentum and mass structure of the KdV equation. The accuracy of reduced solutions, conservation of invariants, and computational speed enhancements facilitated by classical (linear) PCA and KPCA are exemplified through one-dimensional KdV equation.

NIR spectroscopy in conjunction with multivariate analysis for non-destructive characterization of Xuan paper

In the process of conservation mounting, starch paste made from wheat flour is the glue of choice to paste reinforcing strips and backing papers, often Xuan paper, for mending and decorating aged and damaged paper-based Chinese artworks. To keep objects intact and select appropriate conservation materials, this research explored the applicability of near-infrared (NIR) spectroscopy coupled with multivariate analysis for the characterization of contemporary unsized Xuan paper with and without starch. In particular, partial least squares (PLS) regression was used to predict the degree of polymerization (DP) of the paper, i.e., one of the most important properties of paper materials, and principal component analysis (PCA) was used to detect starch and to distinguish between papers with different amounts of starch. Using 12 contemporary unsized Xuan papers, the NIR-PLS method for DP prediction was validated, and the best-performed model was generated using the logarithmic transformation of DP−1 as a response variable, with root mean square error of prediction (RMSEP) of DP 128. The NIR-PCA method was also found to be applicable to separate starch-free papers and papers treated with starch, and the results indicate that the higher the content of starch, the better PCA classification performs. This research provides supporting data for the non-destructive characterization of Xuan paper-based objects and differentiates between Xuan paper before and after treatment with starch.

Use of Raman spectroscopy and PCA for quality evaluation and out-of-specification identification in biopharmaceutical products

Over the past three decades, there was a remarkable growth in the approval of antibody-based biopharmaceutical products. These molecules are notably susceptible to the stresses occurring during drug manufacturing, often leading to structural alterations. A key concern is thus the ability to detect and comprehend these alterations caused by processes, such as aggregation, fragmentation, oxidation levels, as well as the change in protein concentration throughout the process steps, potentially resulting in out-of-spec products.In the present study, Raman spectroscopy, coupled with Principal Component Analysis (PCA), has proven to be an excellent tool for characterizing protein-based products. Notably, it offers the advantages of being minimally invasive, rapid and relatively insensitive to water. Therefore, it was successfully employed to discriminate between various stresses impacting a monoclonal antibody (mAb). The molecule used in this study is a fully human IgG1 fusion protein. Thermal stress was induced by incubating the samples at 50 °C for one month, while oxidative stress was induced by introducing hydrogen peroxide. Additionally, dilutions were performed to explore a broader range of protein concentrations.Specific key bands were identified in the Raman spectra, which facilitated the PCA classification and allowed for their association with distinct changes in the secondary and tertiary structures of the protein. Notably, it was observed that signals corresponding to amino acids exhibited a decrease in intensity with increasing levels of thermal stress, while other alterations were noted in the amide bands. It was shown that changes in the range 2800–3000 cm-1 pertains to the dilution process, while specific peaks of C–H stretching were essential for the discrimination between the oxidative-stressed samples and the thermal and diluted counterparts. Furthermore, the model calibrated on the mAb demonstrated remarkable performance when used to evaluate a different product, e.g. a hormone.

Получение и обработка акустических откликов волн Лэмба в датчиках водных растворов базовых вкусов

In this work, the total acoustic wave losses in 128°Y-LiNbO3 wafers during liquid application due to radiation emission of waves into the liquid medium, as well as its viscosity, conductivity, density and dielectric constant of the liquid analyte are measured. Solutions with 5 basic flavors, widely used for calibration and reference creation in liquid media testing, were selected as test liquids. The measurements were performed in the range of 30...60 MHz. After the measurements, the experimental data were presented in the form of polar histograms, from the comparison of areas and shapes of which the flavor of a particular liquid was determined. Then, a principal component analysis (PCA) method was applied to the same experimental results to describe linear elastic wave deformation processes based on experimental data containing known values of physical wave measurements for different types of liquid media, which was previously used to identify gases and predict the properties of various substances from the linear response of a normal acoustic-electronic wave. In this paper, a comparative analysis of the results obtained using polar histograms and the standard PCA classification method is performed. The advantages of the latter are demonstrated. The results of this study are useful both for the development of acoustic sensors without sensor layers and for the application of machine learning methods to data in other types of sensors.

Classical vs. neural network-based PCA approaches for lossy image compression: Similarities and differences

The paper describes three lossy data compression techniques based on the principal component analysis (PCA), which are compared using the image compression task. The presented approach uses both classical PCA method based on the eigen-decomposition of the image data covariance matrix and two different neural network structures. The first neural structure is a two-layer feed-forward network with supervised learning acting as the so-called autoencoder, and the second one is a single-layered network with an unsupervised learning method commonly known as the generalized Hebbian algorithm. For each compression method mentioned above, the influence of the number of image segments (frames) and the number of eigenvalues/eigenvectors on the compression ratio and the image quality are examined using three different gray-scale test images. The paper also addresses a vital implementation issue regarding selecting appropriate data types to represent the compressed data. The paper’s main conclusion is that the classical PCA method outperforms its neural counterparts, both in terms of the decompressed image quality and the time required to perform the compression procedure. The positive aspect of using neural networks as a tool for PCA-based lossy data compression is that they do not require calculating the correlation matrix explicitly and thus can be used in online data acquisition schemes.

Pilot facial feature recognition and fatigue classification method under high exposure environment

To improve the recognition efficiency of pilot fatigue detection in high exposure environment, a facial feature recognition and fatigue classification method for pilots is proposed. Firstly, the high exposure images are pre-processed with frequency domain light normalization; secondly, a human face detector is trained based on YOLOv5s network model and detects the face, eyes and mouth; again, the fatigue feature values of eyes and mouth are obtained by fusing PERCLOS fatigue detection algorithm; finally, a fatigue classifier is built by RBF-ELM neural network and the fatigue parameters of eyes, mouth and yawning The fatigue parameters of the eye, mouth and yawn features were input to the classifier for fatigue classification. The experimental results show that the fatigue detection speed of the proposed method is up to 310 FPS when computed with GPU, which has higher recognition rate and better robustness compared with the classical PCA, CNN and DCT methods. The method can effectively improve the accuracy of pilot facial feature recognition and eye localization in high exposure environment, and has high practicality for non-contact pilot fatigue detection.

Selection of Landsat-8 Operational Land Imager (OLI) Optimal Band Combinations for Mapping Alteration Zones

In typical alteration extraction methods, e.g., band math and principal component analysis (PCA), the bands or band combinations unitized to extract altered minerals are usually selected based on empirical models or previous rules. This results in significant differences in the alteration of mineral mapping even in the same area, thus greatly increasing the uncertainty of mineral resource prediction. In this paper, an intelligent alteration extraction approach was proposed in which an optimization algorithm, i.e., a genetic algorithm (GA), was introduced into the PCA; this approach is termed GA-PCA and is used for selecting the optimized band combinations of mineralized alterations. The proposed GA-PCA was employed to map iron oxides and hydroxyl minerals using the most commonly adopted multispectral data, i.e., Landsat-8 OLI data, at the Lalingzaohuo polymetallic deposits, China. The results showed that the spectral characteristics of GA-PCA-selected OLI band combinations in the research area were beneficial for enhancing alteration information and were more capable of suppressing the interference of vegetation information. The mapping alteration zones using the GA-PCA approach had a higher agreement with known ore spots, i.e., 25% and 33.3% in ferrous-bearing and hydroxyl-bearing deposits, compared to the classical PCA. Furthermore, two predicted targets (not shown in the classical PCA results) were precisely obtained via analyzing the GA-PCA alteration maps combined with the ore-forming geological conditions of the mine and its tectonic characteristics. This indicated that the intelligent selection of mineral alteration band combinations increased the reliability of remote sensing-based mineral exploration.

The local Universe in the era of large surveys – III. Radial activity profiles of S0 galaxies

ABSTRACT Spatially resolved MaNGA’s optical spectra of 1072 present-day lenticular (S0) galaxies, dimensionally reduced from a principal component analysis (PCA), are used to determine their radial activity structure shaped by any possible nebular ionization source. Activity profiles within $1.5\,$Re are examined in tandem with the mass, age, ellipticity, and kinematics of the stars, as well as environmental density. Among the results of this comparison, we find that the sign of the radial activity gradient of S0s is tightly related to their PCA classification, BPT designation, and star formation status. PCA-passive lenticulars often show low-level, flat activity profiles, although there is also a significant number of systems with positive gradients, while their less common active counterparts generally have negative gradients, usually associated with high SSFRs and, sometimes, moderate Seyfert emission. A fraction of the latter also shows radial activity profiles with positive gradients, which become more abundant with increasing stellar mass regardless of environmental density. Our analysis also reveals that the subset of active S0s with negative gradients experiences at all galactocentric radii a systematic reduction in its median activity level with stellar mass, consistent with expectations for main-sequence galaxies. In contrast, passive S0s with positive gradients show the opposite behaviour. Furthermore, systems whose activity is dominated by star formation are structurally rounder than the rest of S0s, while those classified as Seyfert exhibit higher rotational support. The possibility that negative and positive activity gradients in S0s may result from rejuvenation by two distinct types of minor mergers is raised.

Random projection using random quantum circuits

The random sampling task performed by Google's Sycamore processor gave us a glimpse of the “quantum supremacy era.” This has definitely shed some light on the power of random quantum circuits in this abstract task of sampling outputs from the (pseudo)random circuits. In this paper, we explore a practical near-term use of local random quantum circuits in dimensional reduction of large low-rank data sets. We make use of the well-studied dimensionality reduction technique called the random projection method. This method has been extensively used in various applications such as image processing, logistic regression, entropy computation of low-rank matrices, etc. We prove that the matrix representations of local random quantum circuits with sufficiently shorter depths [∼O(n)] serve as good candidates for random projection. We demonstrate numerically that their projection abilities are not far off from the computationally expensive classical principal components analysis on MNIST and CIFAR-100 image datasets. We also benchmark the performance of quantum random projection against the commonly used classical random projection in the tasks of dimensionality reduction of image data sets and computing von Neumann entropies of large low-rank density matrices. And finally, using variational quantum singular value decomposition, we demonstrate a near-term implementation of extracting the singular vectors with dominant singular values after quantum random projecting a large low-rank matrix to lower dimensions. All such numerical experiments unequivocally demonstrate the ability of local random circuits to randomize a large Hilbert space at sufficiently shorter depths with robust retention of properties of large data sets in reduced dimensions. Published by the American Physical Society 2024

Learning Robust and Sparse Principal Components With the α-Divergence.

In this paper, novel robust principal component analysis (RPCA) methods are proposed to exploit the local structure of datasets. The proposed methods are derived by minimizing the α -divergence between the sample distribution and the Gaussian density model. The α- divergence is used in different frameworks to represent variants of RPCA approaches including orthogonal, non-orthogonal, and sparse methods. We show that the classical PCA is a special case of our proposed methods where the α- divergence is reduced to the Kullback-Leibler (KL) divergence. It is shown in simulations that the proposed approaches recover the underlying principal components (PCs) by down-weighting the importance of structured and unstructured outliers. Furthermore, using simulated data, it is shown that the proposed methods can be applied to fMRI signal recovery and Foreground-Background (FB) separation in video analysis. Results on real world problems of FB separation as well as image reconstruction are also provided.

Efficacy of tyrosine kinase inhibitors examined by a combination of Raman micro-spectroscopy and a deep wavelet scattering-based multivariate analysis framework.

HER2 is a crucial therapeutic target in breast cancer, and the survival rate of breast cancer patients has increased because of this receptor's inhibition. However, tumors have shown resistance to this therapeutic strategy due to oncogenic mutations that decrease the binding of several HER2-targeted drugs, including lapatinib, and confer resistance to this drug. Neratinib can overcome this drug resistance and effectively inhibit HER2 signaling and tumor growth. In the present study, we examined the efficacy of lapatinib and neratinib using breast cancer cells by Raman microscopy combined with a deep wavelet scattering-based multivariate analysis framework. This approach discriminated between control cells and drug-treated cells with high accuracy, compared to classical principal component analysis. Both lapatinib and neratinib induced changes in the cellular biochemical composition. Furthermore, the Raman results were compared with the results of several in vitro assays. For instance, drug-treated cells exhibited (i) inhibition of ERK and AKT phosphorylation, (ii) inhibition of cellular proliferation, (iii) cell-cycle arrest, and (iv) apoptosis as indicated by western blotting, real-time cell analysis (RTCA), cell-cycle analysis, and apoptosis assays. Thus, the observed Raman spectral changes are attributed to cell-cycle arrest and apoptosis. The results also indicated that neratinib is more potent than lapatinib. Moreover, the uptake and distribution of lapatinib in cells were visualized through its label-free marker bands in the fingerprint region using Raman spectral imaging. These results show the prospects of Raman microscopy in drug evaluation and presumably in drug discovery.

A Resource-Efficient Feature Extraction Framework for Image Processing in IoT Devices

Extracting features from image data on Internet of Things (IoT) devices to reduce the amount of data that needs to be uploaded to cloud/edge servers has received increasing attention. However, most of the existing related approaches suffer from two major limitations, (i) low performance and high network traffic, and (ii) a lot of storage resource consumption. To this end, we propose a resource-efficient feature extraction framework for image processing in IoT devices. The proposed framework consists of the edge-assisted extractor generation method and the NestE method. The extractor generated by the edge-assisted extractor generation method can extract the features required by the application, which can not only avoid the IoT device uploading useless feature data but also improve application performance. The proposed NestE generates a nonredundant subextractor by splitting the extractor into multiple subextractors, removing redundant subextractors, and nesting small-capacity subextractors in large-capacity subextractors in a parameter-sharing manner. Compared with deploying multiple independent subextractors on IoT devices, deploying the nonredundant multifunctional extractor can save considerable storage resources and switching overhead. Extensive experimental results show that the proposed framework reduces the storage footprint by approximately 90.7% and switching overhead by approximately 92.4% compared with deploying independent subextractors when using the classical principal component analysis algorithm.

Detecting Outliers Using Modified Recursive PCA Algorithm For Dynamic Streaming Data

Outlier analysis has been widely studied and has produced many methods. However, there is still rare a method to detect outliers for dynamically streaming batch data (online learning). In the present research, a novel online algorithm to detect outliers in such dataset is proposed. Data points are proceeded by applying a modified recursive PCA to predict sequentially parameters of the model; eigenvalues and eigenvectors of the statistical detection model are recursively updated using approximate values by perturbation methods. More specifically, the recursive eigenstructure is obtained from the derivation of the covariance matrix using the first-order perturbation technique. The Mahalanobis distance is then used as an outlier score. Our algorithm performances are evaluated using some metrics, namely accuration, precision, recall, F1-score, AUC-PR, and the execution time. Results show that the proposed online outlier detection is computationally efficient in time and the algorithm's performance effectiveness is comparable to that of the offline outlier detection algorithm via classical PCA.

Real time characterization of atmospheric turbulence using speckle texture

A new method is proposed for the characterization of the atmospheric turbulence strength parameter () based on the principal component analysis (PCA) of the texture of the speckle intensity pattern obtained on long-range propagation of a focused charge +1 vortex beam. Employing the split-step propagation method, datasets containing instantaneous intensity images of the focused vortex beam are generated for three distinct values, specifically , , and , representing medium to high turbulence levels for a 2 km propagation distance. The gray level co-occurrence matrix (GLCM) methodology is employed to extract key texture attributes, like contrast, correlation, homogeneity, and energy from the intensity images. These extracted texture parameters serve as inputs for training a PCA model, enabling the identification of associated values. The PCA analysis exhibits distinct clustering of the first three principal components for each of the three values, forming individual clusters on the PCA plot. Standard deviational ellipses are drawn to clearly demarcate these clusters on the PCA plot. The texture-based PCA classification of atmospheric turbulence was also performed for a focused Gaussian beam. The comparison of PCA plots between vortex and Gaussian beams showed that a pronounced clear separation of values is obtained for the vortex beam. This indicates that the non-zero orbital angular momentum of the vortex beam also plays an important role in achieving the distinct separation of values on the PCA plot. The proposed method can provide efficient real-time turbulence estimation solely on the basis of texture of the instantaneous intensity speckle with prior training and therefore may simplify the estimation of turbulence strength parameter.

Cauchy robust principal component analysis with applications to high-dimensional data sets

Principal component analysis (PCA) is a standard dimensionality reduction technique used in various research and applied fields. From an algorithmic point of view, classical PCA can be formulated in terms of operations on a multivariate Gaussian likelihood. As a consequence of the implied Gaussian formulation, the principal components are not robust to outliers. In this paper, we propose a modified formulation, based on the use of a multivariate Cauchy likelihood instead of the Gaussian likelihood, which has the effect of robustifying the principal components. We present an algorithm to compute these robustified principal components. We additionally derive the relevant influence function of the first component and examine its theoretical properties. Simulation experiments on high-dimensional datasets demonstrate that the estimated principal components based on the Cauchy likelihood typically outperform, or are on a par with, existing robust PCA techniques. Moreover, the Cauchy PCA algorithm we have used has much lower computational cost in very high dimensional settings than the other public domain robust PCA methods we consider.

Consumer Profiles of Sustainable Fruit and Vegetable Consumption in the European Union

Despite the World Health Organization (WHO) advocating a healthy and balanced diet for consumers for many years, inadequate fruit and vegetable (FV) consumption remains a substantial issue with economic, social, environmental, and nutritional implications. This study aims to identify consumer profiles for sustainable fruit and vegetable consumption in the European Union, considering specific demographic and socio-economic factors. The analysis is based on data from the 2019 European Health Interview Survey (EHIS). This article provides a clear understanding of how gender, age, education level, income, and place of residence relate to sustainable FV consumption through a principal component analysis (PCA). By defining consumer profiles linked to sustainable and healthy FV consumption, the study aids in creating cohesive goals for promoting health within European society. The results of the PCA reveal four distinct consumer profiles and enhance our understanding of the factors influencing these profiles. These findings align with previous research, indicating that consumer profiles remain relatively consistent. According to the PCA classification, sustainable fruit and vegetable consumption is more prevalent among women, educated individuals, higher-income consumers, and those over 55 years old. In contrast, the opposite profile consists of consumers with lower income, younger and middle-aged individuals, men, and those with less than a high school education, highlighting the need for effective strategies to promote a healthier diet in this group. Countries with lower economic development, reflected in consumer incomes, deviate the most from the recommended “at least five portions of fruit and vegetables a day” consumption pattern, and do not exhibit a sustainable consumption model. Thus, continued monitoring of consumer profiles is essential, with a focus on both the quantity and frequency of fruit and vegetable consumption, as well as strategies aiming to increase their intake.

RaT: Raman Transformer for highly accurate melanoma detection with critical features visualization

Melanoma is an important cause of death from skin cancer. Early and accurate diagnosis can effectively reduce mortality. But the current diagnosis relies on the experience of pathologists, increasing the rate of misdiagnosis. In this paper, Raman Transformer (RaT) model is proposed by combining Raman spectroscopy and a Transformer encoder to distinguish the Raman spectra of melanoma and normal tissue. To make the spectral data more suitable for the Transformer encoder, we split the Raman spectrum into segments and map them into block vectors, which are then input into the Transformer encoder and classified using the multi-head self-attention mechanism and the Multilayer Perceptron (MLP). The RaT model achieves 99.69% accuracy, 99.61% sensitivity, and 99.82% specificity, which is higher than the classical principal component analysis with the neural network (PCA + NNET) method. In addition, we visualize and explain the fingerprint peaks found by the RaT model and their corresponding biological information. Our proposed RaT model provides a novel and reliable method for processing Raman spectral data, which is expected to help distinguish melanoma from normal cells, diagnose other diseases, and save human lives.