Conventional Principal Component Analysis Research Articles

Separation of underdeveloped from developed cotton fibers by attenuated total reflection Fourier transform infrared spectroscopy

Cotton is an important natural textile commodity and consists of mostly cellulose in developed fibers. Underdeveloped fibers with fewer cellulose were found to cause fiber entanglement and to downgrade the fabric color appearance. Rapid and non-destructive attenuated total reflection Fourier transform infrared (ATR FT-IR) technique was examined to discriminate underdeveloped from developed Upland fibers, which were represented by two cotton near isogenic lines (NILs), Texas Marker-1 (TM-1) and immature fiber (im) mutant that differ in fiber cellulose biosynthesis. Developed NIL fibers were harvested from naturally open bolls in the cotton plant, whereas underdeveloped fibers were collected from unopened bolls in which fibers were still growing at various developmental stages. The spectra of both underdeveloped and developed fibers were analyzed by three algorithms for assessing infrared maturity (MIR), infrared crystallinity (CIIR), and R values, and also by conventional principal component analysis (PCA). Both the first principal component (PC1) score and R values discriminated underdeveloped from developed fibers, but could not differentiate NILs further. Although a single use of MIR or CIIR index could not classify underdeveloped from developed fibers effectively, their combination improved a separation of NILs within underdeveloped or developed fibers. The results suggested that ATR FT-IR spectroscopy with algorithm approach enables the separation of underdeveloped from developed fibers and potentially can be used as fiber phenotype screening method.

Genetic Diversity and Low Stratification of the Population of the United Arab Emirates.

With high consanguinity rates on the Arabian Peninsula, it would not have been unexpected if the population of the United Arab Emirates (UAE) was shown to be relatively homogenous. However, this study of 1000 UAE nationals provided a contrasting perspective, one of a relatively heterogeneous population. Located at the apex of Europe, Asia, and Africa, the observed diversity could be explained by a plethora of migration patterns since the first Out-of-Africa movement. A strategy to explore the extent of genetic variation of the population of the UAE is presented. The first step involved a comprehensive population stratification study that was instructive for subsequent whole genome sequencing (WGS) of suitable representatives (which is described elsewhere). When these UAE data were compared to previous smaller studies from the region, the findings were consistent with a population that is a diverse and admixed group of people. However, rather than sharp and distinctive clusters, cluster analysis reveals low levels of stratification throughout the population. UAE emirates exhibit high within-Emirate-distance/among-Emirate distance ratios. Supervised admixture analysis showed a continuous gradient of ancestral populations, suggesting that admixture on the south eastern tip of the Arabian Peninsula occurred gradually. When visualized using a unique technique that combined admixture ratios and principal component analysis (PCA), unappreciated diversity was revealed while mitigating projection bias of conventional PCA. We observe low population stratification in the UAE in terms of homozygosity versus separation cluster coefficients. This holds for the UAE in a global context as well as for isolated cluster analysis of the Emirati birthplaces. However, the subtle clustering observed in the Emirates reflects geographic proximity and historic migration events. The analytical strategy used here highlights the complementary nature of data from genotype array and WGS for anthropological studies. Specifically, genotype array data were instructive to select representative subjects for WGS. Furthermore, from the 2.3 million allele frequencies obtained from genotype arrays, we identified 46,481 loci with allele frequencies that were significantly different with respect to other world populations. This comparison of allele frequencies facilitates variant prioritization in common diseases. In addition, these loci bear great potential as biomarkers in anthropological and forensic studies.

Detection and discrimination of Carica papaya fungi through the analysis of volatile metabolites by gas chromatography and analysis of variance‐principal component analysis

AbstractConventional detection and identification of the fungi causing postharvest diseases in fruits are time‐consuming, laborious, and can only be performed after the manifestation of symptoms. In this work, an alternative method based on headspace analysis, and which allows the early detection of fungi species frequently found papaya fruit, is presented. Volatile compounds of four in vitro fungi cultures (Alternaria alternata, Colletotrichum gloeosporioides, Fusarium solani, and Lasiodiplodia theobromae) were extracted using solid‐phase microextraction (SPME) and analyzed by gas chromatography coupled with mass spectrometry. The resulting chromatographic fingerprints were explored by conventional principal component analysis (PCA) and analysis of variance (ANOVA)–PCA. Decomposition of the original matrix, according to the factors proposed in the experimental design, by ANOVA before applying the PCA improved the distinction of the control and fungi samples. The main chromatographic peaks referring to the metabolites produced by each species were successfully identified using the proposed analysis. A primary alcohol with five carbons and phenylethyl alcohol were observed in all fungi species and so could be used as an indicative of postharvest disease. Although unique metabolites were detected for all fungi species, only those from C. gloeosporioides and another from F. solani could be surely identified, such as thymolmethyl and 3,6‐dimethylhept‐6‐en‐4‐yn‐3‐ol, respectively. The in vitro results obtained are promising, and it is expected that the biomarkers detected in this work will be useful in future development of methods for the early detection and classification of papaya fungi species.

Fast deflation sparse principal component analysis via subspace projections

ABSTRACTThe implementation of conventional sparse principal component analysis (SPCA) on high-dimensional data sets has become a time consuming work. In this paper, a series of subspace projections are constructed efficiently by using Householder QR factorization. With the aid of these subspace projections, a fast deflation method, called SPCA-SP, is developed for SPCA. This method keeps a good tradeoff between various criteria, including sparsity, orthogonality, explained variance, balance of sparsity, and computational cost. Comparative experiments on the benchmark data sets confirm the effectiveness of the proposed method.

Estimating latent asset-pricing factors

We develop an estimator for latent factors in a large-dimensional panel of financial data that can explain expected excess returns. Statistical factor analysis based on Principal Component Analysis (PCA) has problems identifying factors with a small variance that are important for asset pricing. We generalize PCA with a penalty term accounting for the pricing error in expected returns. Our estimator searches for factors that can explain both the expected return and covariance structure. We derive the statistical properties of the new estimator and show that our estimator can find asset-pricing factors, which cannot be detected with PCA, even if a large amount of data is available. Applying the approach to portfolio data we find factors with Sharpe-ratios more than twice as large as those based on conventional PCA and with smaller pricing errors.

Detection of sensor precision degradation by monitoring second-order statistics

For industrial processes, there are usually a number of measurement sensors equipped for monitoring and control purposes. In practice, sensors may suffer from the precision degradation phenomenon due to several aspects such as aging and ambient interference. This phenomenon may lead to imprecise or even incorrect control commands and indications, so the corresponding fault detection task is of vital importance. In this paper, inspired by the fact that precision degradation of a sensor can result in the increase of the variable’s variance, an algorithm based on second-order statistics analysis is proposed to accomplish the detection task for sensor precision degradation faults. By employing the sliding window technique, second-order statistics of process variables are first extracted. Then, conventional principal component analysis (PCA) is used as a dissimilarity quantification tool, with detection statistics and corresponding control limits established, to perform fault detection. Finally, simulations on a numerical example and the continuous stirred tank reactor (CSTR) benchmark process are performed to illustrate the effectiveness and advantages of the proposed method, in comparison with some existing methods such as PCA, dynamic PCA, and dissimilarity (DISSIM).

Fault Detection in the Tennessee Eastman Benchmark Process Using Principal Component Difference Based on K-Nearest Neighbors

Industrial data usually have nonlinear or multimodal characteristics which do not meet the data assumptions of statistics in principal component analysis (PCA). Therefore, PCA has a lower fault detection rate in industrial processes. Aiming at the above limitations of PCA, a fault detection method using principal component difference based on k-nearest neighbors (Diff-PCA) is proposed in this paper. First, find the k nearest neighbors set of each sample in the training data set and calculate its mean vector. Second, build an augmented vector using each sample and its corresponding mean vector. Third, calculate the loading matrix and score matrix using PCA. Next, calculate the estimated scores using the mean vector of each sample and missing data imputation technique for PCA. At last, build two new statistics using the difference between the real scores and estimated scores to detect faults. In addition, the fault diagnosis method based on contribution plots of monitored variables is also proposed in this paper. In Diff-PCA, the difference skill can eliminate the impact of the nonlinear and multimodal structure on fault detection. Meanwhile, the monitored subspaces by the two new statistics are different from that by T2 and SPE in PCA. The efficiency of the proposed strategy is implemented in two numerical cases (nonlinear and multimode) and the Tennessee Eastman (TE) processes. The fault detection results indicate that Diff-PCA outperforms the conventional PCA, Kernel PCA, dynamic PCA, principal component-based k nearest neighbor rule and k nearest neighbor rule.

Regional Principal Component Analysis Network With the Rolling Guidance Filter for Classifying the Hyperspectral Images

Because conventional PCANET approach is that the conventional PCANET performs the PCA for all the segments of all the training pixel vectors, and this does not capture the difference between different segments of the same training pixel vectors, classification accuracy is not high. This paper proposes to employ a regional principal component analysis network with the rolling guidance filter (RPCANET_RGF) for performing the hyperspectral image (HSI) classification with few training samples. Regional principal component analysis network (RPCANET) proposed in this paper performs the PCA for each segment of all training pixel vectors. Besides, the rolling guidance filter (RGF) is used to remove the spatial noise and to enhance the edges of the HSIs. Different from the conventional convolutional neural networks (CNNs), the coefficients of the filters are obtained by performing the principal component analysis (PCA) on the regional segments of HSIs. This approach is also different from the conventional principal component analysis network (PCANET). Here, different segments of the same pixel image are processed by different Filters. Since the RPCANET_RGF is a general learning method that obtains the filter coefficients directly from the HSIs, the back propagation based training is not required. Hence, the RPCANET_RGF requires a less computational power for performing the training compared to the CNN. Besides, as the RPCANET_RGF can make use of both the spectral information and the spatial information for performing the classification, the computer numerical simulation results show that the classification accuracy achieved by the RPCANET_RGF is higher than that by the conventional PCANET and other state of the art methods.

Testing for Structural Changes in Large Dimensional Factor Models via Discrete Fourier Transform

We propose a new test for structural changes in large dimensional factor models via a discrete Fourier transform (DFT) approach. If structural changes occur, the conventional principal component analysis fails to estimate common factors and factor loadings consistently. The estimated residuals will contain information about structural changes. Therefore, we can compare the DFT of the estimated residuals with the null (zero) spectrum implied by no structural change. The proposed test is powerful against both smooth structural changes and abrupt structural breaks with a possibly unknown number of breaks and unknown break dates in factor loadings. It can detect a class of local alternatives at the parametric rate. As a result, the test is asymptotically more efficient than the existing tests in the factor model literature. And our test is also robust to serial and cross-sectional dependence of unknown form without having to estimate any long-run variance-covariance matrix. Moreover, it is easy to implement and tuning parameter-free. Monte Carlo studies demonstrate its reasonable size and excellent power in detecting various forms of structural changes in factor loadings. In an application to the U.S. macroeconomic data, we find significant and robust evidence of time-varying factor loadings.

Root causality analysis at early abnormal stage using principal component analysis and multivariate Granger causality

As fault detection technologies have been developed, process fault diagnosis at early abnormal stage has come to be considered a major problem. In this work, a method to analyze the root cause of faults is developed to provide proper information at the early abnormal stage. First, principal component analysis (PCA) is used for the early detection of the process fault. Then, the contributions, from which the normal portion is removed, are decomposed by singular value decomposition (SVD) method to select the hierarchical sensors. Finally, the multivariate Granger causality (MVGC) method is used to construct the sensor causalities using the hierarchical sensors. The developed methodology is verified using the liquefied natural gas fractionation process model, which embeds a sufficient number of highly correlated sensors. The results are compared with the conventional principal component analysis method and amplification of the residual contribution method to verify the advantages of the proposed method.

Visualization of Chemical Space using Kernel Based Principal Component Research

Principal Component analysis (PCA) is one of the important and popular multivariate statistical methods applied over various data modeling applications. Traditional PCA handles linear variance in molecular descriptors or features. Handling complicated data by standard PCA will not be very helpful. This drawback can be handled by introducing kernel matrix over PCA. Kernel Principal Component Analysis (KPCA) is an extension of conventional PCA which handles non-linear hidden patterns exists in variables. It results in computational efficiency for data analysis and data visualization. In this paper, KPCA has been applied over dug-likeness dataset for visualization of non-linear relations exists in variables.

Principal component analysis based on intuitionistic fuzzy random variables

This paper suggests a principal component analysis for intuitionistic fuzzy data. For this purpose, first a notion of intuitionistic fuzzy random variable was introduced and discussed. The concept of correlation and its natural estimator between two intuitionistic fuzzy random variables was also developed and the main properties of the proposed correlation criteria were investigated. Then, the conventional principal component analysis was extended for intuitionistic fuzzy random variables. In this regard, score and loading plots were extended to analyze the first and the second principle components. A possible application of the proposed method was also illustrated via an practical psychology-relevant example.

Improved Clutter Removal in GPR by Robust Nonnegative Matrix Factorization

The clutter encountered in the ground-penetrating radar (GPR) system severely decreases the visibility of subsurface objects, thus highly degrading the performance of the target detection algorithms. This letter presents a new clutter removal method based on nonnegative matrix factorization (NMF). The raw GPR data are represented as the sum of low-rank and sparse matrices, which correspond to the clutter and target components, respectively. The low-rank and sparse decomposition is performed using a robust version of NMF called RNMF. Although similar to the robust principal component analysis (PCA) (RPCA), which is recently widely used in image processing applications as well as in GPR, the proposed method is faster and has enhanced results. The state-of-the-art clutter removal methods, morphological component analysis (MCA), RPCA, besides the conventional PCA, have been included for comparison for both simulated and real data sets. The visual and quantitative results demonstrate that the proposed RNMF method outperforms the others. Moreover, it is 25 times faster than the RPCA for the given regularization parameter values.

Hash-tree PCA: accelerating PCA with hash-based grouping

In data mining or machine learning, one of the most commonly used feature extraction techniques is principal component analysis (PCA). However, it performs poorly on a large dataset. In this paper, we propose a new method of accelerating conventional PCA, named hash-tree PCA. It samples the objects that are similar to each other without losing the original data distribution. First, it explores similar objects and stores them in hash tables. Afterward, it samples a certain number of the objects from each hash table and creates a new dataset with a reduced number of objects. Finally, it executes PCA on the sampled dataset. Experimental results show that our method outperforms the PCA and fast PCA methods.

Type-2 Fuzzy PCA Approach in Extracting Salient Features for Molecular Cancer Diagnostics and Prognostics.

Machine learning is becoming a powerful tool for cancer diagnosis and prognosis based on classification using high dimensional molecular data. However, extracting classification features from high-dimensional datasets remains a challenging problem. Principal component analysis (PCA) is a widely used method for dimensionality reduction. However, it is well-known that PCA and most PCA-based feature extraction methods are sensitive to noise, which may affect the accuracy of the subsequent classification. To address this problem, here we have proposed a robust fuzzy principal component analysis (PCA) with interval type-2 (IT-2) fuzzy membership functions for feature extraction. We have tested the performance of three widely used classifiers using the features extracted by proposed approaches and other feature extraction methods - PCA-based feature extraction methods (i.e. conventional PCA and fuzzy PCA), linear discriminant analysis (LDA), and support vector machine recursive feature elimination (SVM-RFE). The proposed feature extraction approaches showed better performance on cancer transcriptome and proteome datasets.

Fault Detection Method Using Multi-mode Principal Component Analysis Based on Gaussian Mixture Model for Sewage Source Heat Pump System

This paper presents an algorithm for fault detection of a sewage heat pump system by designing multi-mode principal component analysis with Gaussian mixture model. If the heat pump system fails, the loss of energy and time is enormous, therefore the fault detection of the system is important. For this purpose, this study proposes a fault detection method using multi-mode principal component analysis with Gaussian mixture model. The data were clustered into multi-mode of Gaussian on principal component subspace. Based on the multi-model, the values of Hotelling’s T2 and SPE were calculated and used for the fault detection as indexes that are compared performance with clustering model using k-means and k-medoids algorithm as well as conventional PCA. Actual data of the sewage heat pump were used to verify the proposed method. The results of the fault detection performance show that the proposed model shows the best performance of fault detection among the conventional, k-means, and k-medoids PCA models.

Weighted preliminary-summation-based principal component analysis for non-Gaussian processes

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.

Spatial‐spectral analysis method using texture features combined with PCA for information extraction in hyperspectral images

AbstractThis work proposes a new method to treat spatial and spectral information interactively. The method extracts spatial features, ie, variogram, gray‐level co‐occurrence matrix (GLCM), histograms of oriented gradients (HOG), and local binary pattern (LBP) features, from each wavelength image of hypercube and principal component analysis (PCA) is applied on this spatial feature matrix to identify wavelength‐dependent variation in spatial patterns. Resultant image is obtained by projecting the score values to the original data. Three datasets, including a synthetic hyperspectral image (Dataset 1), a set of real hyperspectral images of salmon fillets (Dataset 2), and remote‐sensing images (Dataset 3), were utilized to evaluate the performance of the proposed method. Results from Dataset 1 showed that the spatial‐spectral methods had the potential of reducing baseline offset noise. Dataset 2 revealed that spatial‐spectral methods can alleviate noisy pixels with strong signal and reduce shadow effects. In addition, substantial improvements were obtained in case of classification between white stripe and red muscle pixels by using the HOG‐based approach with correct classification rate (CCR) of 0.97 compared with the models directly built from raw and standard normal variate (SNV) preprocessed spectra (CCR = 0.94). Samson image of Dataset 3 suggested the flexibility and effectiveness of the proposed method by improving CCR of 0.96 using conventional PCA on SNV pretreated spectra to 0.98 using GLCM‐based approach on SNV preprocessed spectra. Overall, experimental results demonstrated that the spatial‐spectral methods can improve the results found by using the spectral information alone because of the spatial information provided.

Effective feature extraction through segmentation-based folded-PCA for hyperspectral image classification

ABSTRACT The remote sensing hyperspectral images (HSIs) usually comprise many important information of the land covers capturing through a set of hundreds of narrow and contiguous spectral wavelength bands. Appropriate classification performance can only offer the required knowledge from these immense bands of HSI since the classification result is not reasonable using all the original features (bands) of the HSI. Although it is not easy to calculate the intrinsic features from the bands, band (dimensionality) reduction techniques through feature extraction and feature selection are usually applied to increase the classification result and to fix the curse of dimensionality problem. Though the Principal Component Analysis (PCA) has been commonly adopted for the feature reduction of HSI, it can often fail to extract the local useful characteristics of the HSI for effective classification as it considers the global statistics of the HSI. Consequently, Segmented-PCA (SPCA), Spectrally-Segmented-PCA (SSPCA), Folded-PCA (FPCA) and Superpixelwise PCA (SuperPCA) have been introduced for better feature extraction of HSI in diverse ways. In this paper, feature extraction through SPCA & FPCA and SSPCA & FPCA, termed as Segmented-FPCA (SFPCA) and Spectrally-Segmented-FPCA (SSFPCA) respectively, has further been improved through applying FPCA on the highly correlated or spectrally separated bands’ segments of the HSI rather than not applying the FPCA on the entire dataset directly. The proposed methods are compared and analysed for a real mixed agricultural and an urban HSI classification using per-pixel SVM classifier. The experimental result shows that the classification performance using SSFPCA and SFPCA outperforms that of using conventional PCA, SPCA, SSPCA, FPCA, SuperPCA and using the entire original dataset without employing any feature reduction. Moreover, the proposed feature extraction methods provide the least memory and computation cost complexity.

Comprehending the IoT cyber threat landscape: A data dimensionality reduction technique to infer and characterize Internet-scale IoT probing campaigns

The resource-constrained and heterogeneous nature of Internet-of-Things (IoT) devices coupled with the placement of such devices in publicly accessible venues complicate efforts to secure these devices and the networks they are connected to. The Internet-wide deployment of IoT devices also makes it challenging to operate security solutions at strategic locations within the network or to identify orchestrated activities from seemingly independent malicious events from such devices. Therefore, in this paper, we initially seek to determine the magnitude of IoT exploitations by examining more than 1 TB of passive measurement data collected from a/8 network telescope and by correlating it with 400 GB of information from the Shodan service. In the second phase of the study, we conduct in-depth discussions with Internet Service Providers (ISPs) and backbone network operators, as well as leverage geolocation databases to not only attribute such exploitations to their hosting environment (ISPs, countries, etc.) but also to classify such inferred IoT devices based on their hosting sector type (financial, education, manufacturing, etc.) and most abused IoT manufacturers. In the third phase, we automate the task of alerting realms that are determined to be hosting exploited IoT devices. Additionally, to address the problem of inferring orchestrated IoT campaigns by solely observing their activities targeting the network telescope, we further introduce a theoretically sound technique based on L1-norm PCA, and validate the utility of the proposed data dimensionality reduction technique against the conventional L2-norm PCA. Specifically, we identify “in the wild” IoT coordinated probing campaigns that are targeting generic ports and campaigns specifically searching for open resolvers (for amplification purposes). The results reveal more than 120,000 Internet-scale exploited IoT devices, some of which are operating in critical infrastructure sectors such as health and manufacturing. We also infer 140 large-scale IoT-centric probing campaigns; a sample of which includes a worldwide distributed campaign where close to 40% of its population includes video surveillance cameras from a single manufacturer, and another very large inferred coordinated campaign consisting of more than 50,000 IoT devices. The reported findings highlight the insecurity of the IoT paradigm at large and thus demonstrate the importance of understanding such evolving threat landscape.