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Journal articles on the topic 'Principle component analysis'

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Published: 4 June 2021

Last updated: 31 January 2023

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1

Wen, Chenglin, Tianzhen Wang, and Jing Hu. "Relative principle component and relative principle component analysis algorithm." Journal of Electronics (China) 24, no. 1 (January 2007): 108–11. http://dx.doi.org/10.1007/s11767-006-0097-2.

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2

Polyak, B. T., and M. V. Khlebnikov. "Principle component analysis: Robust versions." Automation and Remote Control 78, no. 3 (March 2017): 490–506. http://dx.doi.org/10.1134/s0005117917030092.

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3

Chen, Songcan, and Yulian Zhu. "Subpattern-based principle component analysis." Pattern Recognition 37, no. 5 (May 2004): 1081–83. http://dx.doi.org/10.1016/j.patcog.2003.09.004.

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4

Wu, Danyang, Han Zhang, Feiping Nie, Rong Wang, Chao Yang, Xiaoxue Jia, and Xuelong Li. "Double-Attentive Principle Component Analysis." IEEE Signal Processing Letters 27 (2020): 1814–18. http://dx.doi.org/10.1109/lsp.2020.3027462.

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5

Arab, Abbas, Jamila Harbi, and Amel Abbas. "Image Compression Using Principle Component Analysis." Al-Mustansiriyah Journal of Science 29, no. 2 (November 17, 2018): 141. http://dx.doi.org/10.23851/mjs.v29i2.256.

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Principle component analysis produced reduction in dimension, therefore in our proposed method used PCA in image lossy compression and obtains the quality performance of reconstructed image. PSNR values increase when the number of PCA components is increased and CR, MSE, and other error parameters decreases when the number of components is increased.

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6

Kim, Je-Nam, Mun-Ho Ryu, Ho-Rim Choi, and Yoon-Seok Yang. "Anatomy Calibration of Inertial Measurement Unit Using a Principle Component Analysis." International Journal of Bio-Science and Bio-Technology 5, no. 6 (December 31, 2013): 181–90. http://dx.doi.org/10.14257/ijbsbt.2013.5.6.19.

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7

Dholakia, Stuti G., and Chetna D. Bhavsar. "Factor recovery by principle component analysis and harris component analysis." Asian Journal of Research in Social Sciences and Humanities 7, no. 7 (2017): 177. http://dx.doi.org/10.5958/2249-7315.2017.00376.8.

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8

Zhang, Yan, and Shi Sheng Zhou. "Research on Reconstruction of Spectral Reflectance Based on Principal Component Analysis." Applied Mechanics and Materials 262 (December 2012): 53–58. http://dx.doi.org/10.4028/www.scientific.net/amm.262.53.

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Traditional color reproduction technology based on the Metamerism principle, the disadvantage is that different observer condition leads to different color appearance.To fulfill the color consistency, the spectrum reflectance of the object color sample need to be reconstructed. The principal component analysis makes use of the linear combination of a few principal components to reconstruct the spectral reflectance of sample. This paper analyzes the 31*31 matrix of Munsell spectral data by the principle component analyze method and achieves the principal component for spectrum reflectance. The numbers of principal components are identified as six by discussing the variance contribution rate. Spectral reconstruction of four Munsell testing samples makes use of first six principal components, which has met the accuracy requirements. Research shows that the reconstruction of spectral accuracy decreased when training samples and testing samples belong to the different database.

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9

Bowden, R., T. A. Mitchell, and M. Sarhadi. "Cluster based nonlinear principle component analysis." Electronics Letters 33, no. 22 (1997): 1858. http://dx.doi.org/10.1049/el:19971300.

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10

Purwandari, Endina Putri, Aan Erlansari, Andang Wijanarko, and Erich Adinal Adrian. "Face sketch recognition using principal component analysis for forensics application." Jurnal Teknologi dan Sistem Komputer 8, no. 3 (April 24, 2020): 178–84. http://dx.doi.org/10.14710/jtsiskom.2020.13422.

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Recognition of human faces in forensics applications can be identified through the Sketch recognition method by matching sketches and photos. The system gives five criminal candidates who have similarities to the sketch given. This study aims to perform facial recognition on photographs and sketches using Principal Component Analysis (PCA) as feature extraction and Euclidean distance as a calculation of the distance of test images to training images. The PCA method was used to recognize facial images from pencil sketch drawings. The system dataset is in the form of photos and sketches in the CUHK Face Sketch database consists of 93 photos and 93 sketches, and personal documentation consists of five photos and five sketches. The sketch matching application to training data produces an accuracy of 76.14 %, precision of 91.04 %, and recall of 80.26 %, while testing with sketch modifications produces accuracy and recall of 95 % and precision of 100 %.

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11

Aini Abdul Wahab, Nurul, and Shamshuritawati Sharif. "Rice Odours’ Readings Investigation Using Principal Component Analysis." International Journal of Engineering & Technology 7, no. 2.29 (May 22, 2018): 488. http://dx.doi.org/10.14419/ijet.v7i2.29.13803.

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The use of electronic nose (e-nose) devices plus principal component analysis can help the process of categorizing the 16 different rice into its type. Generally, the physical feature of an e-nose own more than one hole to capture the odour of rice. For example, the portable e-nose so-called Insniff does have 10 holes (or variables). In this situations, we will have a dataset that consist high-dimension dataset where lead to the presence of interdependencies between all variables under study. Therefore, this study is presented to investigate the odour of rice for identifying the most important variables contributing to the rice odour readings. The principal component analysis (PCA) is implemented to determine the component that best represent the all 10 variables in order to eliminate the interdependency problem, and (2) to identify which variable is considered as important and influential to the newly-formed principle component (PC). The results from PCA suggested that the first two principle components is chosen. It is based on three assessments which are Kaiser’s criterion larger than 1, cumulative proportion of total variance, and scree plot. These two principle components explained 89% of total variance. Results showed that sensor 1 (0.931) and sensor 2 (0.966) are the two important variables that highly contribute to PC1. On the other hand, for PC2, the highest contribution is from sensor 8 (0.828). This study demonstrate that PCA is effective for investigating rice odour readings.

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12

B S, Lokasree. "Data Analysis and Data Classification in Machine Learning using Linear Regression and Principal Component Analysis." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 835–44. http://dx.doi.org/10.17762/turcomat.v12i2.1092.

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In this paper step-by-step procedure to implement linear regression and principal component analysis by considering two examples for each model is explained, to predict the continuous values of target variables. Basically linear regression methods are widely used in prediction, forecasting and error reduction. And principle component analysis is applied for facial recognition, computer vision etc. In Principal component analysis, it is explained how to select a point with respect to variance. And also Lagrange multiplier is used to maximize the principle component function, so that optimized solution is obtained

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13

Bischoff, Petra, Eckehard Scharein, Gunter N. Schmidt, Georg von Knobelsdorff, Burkhart Bromm, and Jochen Schulte am Esch. "Topography of Clonidine-induced Electroencephalographic Changes Evaluated by Principal Component Analysis." Anesthesiology 92, no. 6 (June 1, 2000): 1545–52. http://dx.doi.org/10.1097/00000542-200006000-00010.

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Background Principal component analysis is a multivariate statistical technique to facilitate the evaluation of complex data dimensions. In this study, principle component analysis was used to reduce the large number of variables from multichannel electroencephalographic recordings to a few components describing changes of spatial brain electric activity after intravenous clonidine. Methods Seven healthy volunteers (age, 26 +/- 3 [SD] yr) were included in a double-blind crossover study with intravenous clonidine (1.5 and 3.0 microg/kg). A spontaneous electroencephalogram was recorded by 26 leads and quantified by standard fast Fourier transformation in the delta, theta, alpha, and beta bands. Principle component analysis derived from a correlation matrix calculated between all electroencephalographic leads (26 x 26 leads) separately within each classic frequency band. The basic application level of principle component analysis resulted in components representing clusters of electrodes positions that were differently affected by clonidine. Subjective criteria of drowsiness and anxiety were rated by visual analog scales. Results Topography of clonidine-induced electroencephalographic changes could be attributed to two independent spatial components in each classic frequency band, explaining at least 85% of total variance. The most prominent effects of clonidine were increases in the delta band over centroparietooiccipital areas and decreases in the alpha band over parietooccipital regions. Clonidine administration resulted in subjective drowsiness. Conclusions Data from the current study supported the fact that spatial principle component analysis is a useful multivariate statistical procedure to evaluate significant signal changes from multichannel electroencephalographic recordings and to describe the topography of the effects. The clonidine-related changes seen here were most probably results of its sedative effects.

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14

Uzzafer, Masood. "Principle Component Analysis of Function Point Elements." International Journal of Advanced Science and Technology 91 (July 31, 2016): 39–48. http://dx.doi.org/10.14257/ijast.2016.91.04.

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15

Shams, Mohamed A. Bin. "Fault Identification using Kernel Principle Component Analysis." IFAC Proceedings Volumes 44, no. 1 (January 2011): 4320–25. http://dx.doi.org/10.3182/20110828-6-it-1002.03747.

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Kasban, H., H. Arafa, and S. M. S. Elaraby. "Principle component analysis for radiotracer signal separation." Applied Radiation and Isotopes 112 (June 2016): 20–26. http://dx.doi.org/10.1016/j.apradiso.2016.03.005.

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17

Raffey, Dr Mohd Abdul. "Sentimental Analysis of Tweets using Principle Component Analysis Technique." International Journal for Research in Applied Science and Engineering Technology V, no. XI (November 22, 2017): 1601–4. http://dx.doi.org/10.22214/ijraset.2017.11229.

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18

Yong, Ching Yee, Rubita Sudirman, Nasrul Humaimi Mahmood, and Kim Mey Chew. "Human Hand Movement Analysis Using Principle Component Analysis Classifier." Applied Mechanics and Materials 284-287 (January 2013): 3126–30. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3126.

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This study investigates and acts as a trial clinical outcome for human motion and behavior analysis in order to investigate human arm movement during jogging and walking. It was developed to analyze and access the quality of human motion that can be used in hospitals, clinics and human motion researches. It aims to establish how widespread the movement and motion of arm will bring to effect of human in life. An experiment was set up in a laboratory environment with conjunction of analyzing human motion and its behavior. The instruments demonstrate adequate internal consistency of optimum scatter plot in gyroscope and accelerometer for pattern classification. PCA used in this study was successfully differentiate and classify

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19

Dong, Jian Chao, Tie Jun Yang, Xin Hui Li, Zhi Jun Shuai, and You Hong Xiao. "Identification of Excitation Source Number Using Principal Component Analysis." Advanced Materials Research 199-200 (February 2011): 850–57. http://dx.doi.org/10.4028/www.scientific.net/amr.199-200.850.

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Principal component analysis (PCA), serving as one of the basic blind signal processing techniques, is extensively employed in all forms of analysis for extracting relevant information from confusing data sets. The principle of PCA is explained in this paper firstly, then the simulation and experiment are carried out to a simply supported beam rig, and PCA is used in frequency domain to identify sources number of several cases. Meanwhile principal components (PCs) contribution coefficient and signal to noise ratio between neighboring PCs (neighboring SNR) are introduced to cutoff minor components quantificationally. The results show that when observation number is equal to or larger than source number and additive noise is feebleness, accurate prediction of the number of uncorrelated excitation sources in a multiple input multiple output system could be obtained by principal component analysis.

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20

Xu, Xiaoming, and Chenglin Wen. "Fault Diagnosis Method Based on Information Entropy and Relative Principal Component Analysis." Journal of Control Science and Engineering 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/2697297.

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In traditional principle component analysis (PCA), because of the neglect of the dimensions influence between different variables in the system, the selected principal components (PCs) often fail to be representative. While the relative transformation PCA is able to solve the above problem, it is not easy to calculate the weight for each characteristic variable. In order to solve it, this paper proposes a kind of fault diagnosis method based on information entropy and Relative Principle Component Analysis. Firstly, the algorithm calculates the information entropy for each characteristic variable in the original dataset based on the information gain algorithm. Secondly, it standardizes every variable’s dimension in the dataset. And, then, according to the information entropy, it allocates the weight for each standardized characteristic variable. Finally, it utilizes the relative-principal-components model established for fault diagnosis. Furthermore, the simulation experiments based on Tennessee Eastman process and Wine datasets demonstrate the feasibility and effectiveness of the new method.

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21

SHIN, H. C., H. N. KIM, and W. J. SONG. "A Simple Adaptive Algorithm for Principle Component and Independent Component Analysis." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E91-A, no. 5 (May 1, 2008): 1265–67. http://dx.doi.org/10.1093/ietfec/e91-a.5.1265.

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22

Wang, Zhiliang, Yalin Sun, and Peng Li. "Functional Principal Components Analysis of Shanghai Stock Exchange 50 Index." Discrete Dynamics in Nature and Society 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/365204.

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The main purpose of this paper is to explore the principle components of Shanghai stock exchange 50 index by means of functional principal component analysis (FPCA). Functional data analysis (FDA) deals with random variables (or process) with realizations in the smooth functional space. One of the most popular FDA techniques is functional principal component analysis, which was introduced for the statistical analysis of a set of financial time series from an explorative point of view. FPCA is the functional analogue of the well-known dimension reduction technique in the multivariate statistical analysis, searching for linear transformations of the random vector with the maximal variance. In this paper, we studied the monthly return volatility of Shanghai stock exchange 50 index (SSE50). Using FPCA to reduce dimension to a finite level, we extracted the most significant components of the data and some relevant statistical features of such related datasets. The calculated results show that regarding the samples as random functions is rational. Compared with the ordinary principle component analysis, FPCA can solve the problem of different dimensions in the samples. And FPCA is a convenient approach to extract the main variance factors.

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23

M. Saleh, Maysoun, Abdelmoaty B. Elabd, and Abdel Rahman Mohammad Al Tawaha. "Principle Component Analysis among Exotic and Egyptian Rice Genotypes." Advances in Environmental Biology 15, no. 1 (January 1, 2007): 16–22. http://dx.doi.org/10.22587/aeb.2021.15.1.3.

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Twenty-five rice genotypes with two Egyptian rice varieties: Giza 178 and Sakha 105 were all cultivated in a Randomized Complete Block Design (RCBD) with two replications under saline soil at at El_Sirw Agriculture Research Station northern part of Delta, Egypt during the growing season 2012. This study aimed to evaluate the potential divergence among tested genotypes and to define the role of agronomic traits in the total variation by using principle component analysis. Results showed that high variability was noticed between the testes genotypes, and indicated that only four principle components PC1, PC2, PC3 and PC4 were significant as they had an Eigenvalue greater than 1.0 (2.3967, 2.1444, 1.7225, 1.0618) respectively, explained together 73.3% of existed variation between genotypes. The first principle component PC1 explained the highest variation 24%, followed by other components PC2, PC3 and PC4 which explained (21.4, 17.2, 10.6) % of the total variation. Results also revealed that days to flowering and number of filled grains per panicle were associated with PC1, whereas three traits (tillers per plant, panicles number per plant and thousand grain weight) were gathered in PC2, and the PC3 consisted of (panicle length, panicle fertility and grain yield per plant), while the last component PC4 contained both traits flag leaf area and plant height. It was concluded that the divergence between rice genotypes in our study provide a wide genetic base for breeders to improve rice

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Ma Yuan, L Qun-Bo, Liu Yang-Yang, Qian Lu-Lu, and Pei Lin-Lin. "Image sparsity evaluation based on principle component analysis." Acta Physica Sinica 62, no. 20 (2013): 204202. http://dx.doi.org/10.7498/aps.62.204202.

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Liu, Yang, Hong Li, Si-Yu Li, Yong-Ping Li, and Xinmin Zhang. "Cosmic reionization study: principle component analysis after Planck." Journal of Cosmology and Astroparticle Physics 2016, no. 02 (February 18, 2016): 046. http://dx.doi.org/10.1088/1475-7516/2016/02/046.

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26

Tanaka, Masahiro, Fumiaki Takeda, and Yasuyo Michiyuki. "Bank Note Recognition by Probabilistic Principle Component Analysis." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 1999 (May 5, 1999): 25–30. http://dx.doi.org/10.5687/sss.1999.25.

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27

Chakraborty, Dulal, Sanjit Kumar Saha, and Md Al-Amin Bhuiyan. "Face Recognition using Eigenvector and Principle Component Analysis." International Journal of Computer Applications 50, no. 10 (July 28, 2012): 42–49. http://dx.doi.org/10.5120/7811-0947.

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Shi, Xiaoran, Feng Zhou, Mingliang Tao, and Zijing Zhang. "Human Movements Separation Based on Principle Component Analysis." IEEE Sensors Journal 16, no. 7 (April 2016): 2017–27. http://dx.doi.org/10.1109/jsen.2015.2509185.

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Liu, Yong, Biao Ma, Yu Yan, and Chang Song Zheng. "Failure Prediction and Wear State Evaluation of Power Shift Steering Transmission." Applied Mechanics and Materials 741 (March 2015): 183–87. http://dx.doi.org/10.4028/www.scientific.net/amm.741.183.

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Within the vehicle transmission, the friction surfaces of mechanical parts were consecutively worn-out and ultimately up to the degradation failures. For assessing the wear progress effectively, wear particles should be generally monitored by measuring the element concentration through Atomic emission (AE) spectroscopy. Herein, the spectral data sampled from life-cycle test has been processed by both the Principal Component Analysis (PCA) and further Kernel Principal Component Analysis (KPCA). Results show that KPCA acts more effectively in variable-dimensions reduction due to fewer principle components and higher cumulative contributing rate. To detect the threshold point at where the wear-stage upgraded, the Fuzzy C-means clustering algorithm was applied to process the eigenvalues of principle components. Furthermore, it is demonstrated that the principle components relate to the worn-out state of friction pairs or transmission parts. In general, the introduction of KPCA has contributed to assess the wear-stage at where the machine situates and the accurate worn-out state of various transmission parts.

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Honório, Bruno César Zanardo, Alexandre Cruz Sanchetta, Emilson Pereira Leite, and Alexandre Campane Vidal. "Independent component spectral analysis." Interpretation 2, no. 1 (February 1, 2014): SA21—SA29. http://dx.doi.org/10.1190/int-2013-0074.1.

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Spectral decomposition techniques can break down the broadband seismic records into a series of frequency components that are useful for seismic interpretation and reservoir characterization. However, it is laborious and time-consuming to analyze and to interpret each seismic frequency volume taking all the usable seismic bandwidth. In this context, we propose a multivariate technique based on independent component analysis (ICA) with the goal of choosing the spectral components that best represent the whole seismic spectrum while keeping the main geological information. The ICA-based method goes beyond the Gaussian assumption and takes advantage of higher order statistics to find a new set of variables that are independent of each other. The independence between two components is a more general statistical concept than the noncorrelation and, in principle, allows the extraction of more significant information from the data. We have tested four different contrast functions to estimate the independent components (ICs), which we could verify a better channel system identification depending on the contrast function used. By stacking the ICs in the red-green-blue color space, we could represent the main information in a single, good quality image. To illustrate the proposed method, we have applied it to a seismic volume which was acquired over the F3 block in the Dutch sector of the North Sea. We also compared the results with those obtained by principal component analysis. In this case, the ICA-based method could generate a better image and faithfully delineate a channel system presented in the studied seismic volume.

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31

Azhari, Budi, and Ade Irfan. "MODEL-ELICITING ACTIVITIES DALAM MENGANALISIS KREATIVITAS PEMECAHAN MASALAH MATEMATIKA PADA MAHASISWA PENDIDIKAN MATEMATIKA DI PTKIN ACEH." Al Khawarizmi: Jurnal Pendidikan dan Pembelajaran Matematika 2, no. 1 (February 26, 2019): 1. http://dx.doi.org/10.22373/jppm.v2i1.4495.

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This study aimed to know the components of the student creativity in problem solving that can be achieved through Model Eliciting Activities in preservice’s teacher of mathematics education of PTKIN in Aceh. This Research used qualitatif approach, since in this study want to describe the reality on the field namely data about the studets’ creativity in solving maths problem. The result showed that the componentsof flexibility obtained by contruction principle, the reality principle, and the self-just my assesment principle. Even so, there are student who got no flexibility with the third principle of the MEA, but only with the principles, the only reality MEA principle and the effective prototype principle. Mean while, the component of fluency is gained student by analysis of the construct documentation principle. The last component of creativity that is obtained through the construct shareability and reusability and the effective prototype principle. However there are students who do not obtain theses components due to the absence of new student-generated in carrying out problem-solving.

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Feng, Yusi, Hongkai Chen, and Xin Zheng. "Component analysis of ancient glass products based on hierarchical analysis clustering algorithm." Highlights in Science, Engineering and Technology 21 (December 4, 2022): 180–85. http://dx.doi.org/10.54097/hset.v21i.3155.

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First, the attachment is pre-processed, and after the abnormal data are removed side by side, the bar chart is drawn to preliminarily analyze the relationship that lead-barium glass is easier to weathering than high potassium glass. Then the chi-square test is carried out to find that whether the weathering of the glass cultural relics surface is related to the glass type of the cultural relics, but there is no obvious relationship with the decoration and color of the cultural relics. Secondly, the statistical model of one-way ANOVA was established, and the difference analysis of various chemical components was conducted before and after the two types of glass weathering. The chemical components that passed the difference test had significant statistical rules. Finally, multiple linear regression equations are constructed to predict the chemical composition content before weathering. The 14 chemical components of glass were regarded as 14 indicators, and the principal component analysis method was used to calculate the principal component contribution rate and the cumulative contribution rate, and then the two principal components were determined. Then the principal component analysis was used to cluster the indicators, and the hierarchical clustering algorithm was used to generate lineage maps. According to the elbow principle, the number of subcategories of high potassium glass is 3, and the number of subcategories of lead-barium glass is 4, so as to divide the chemical composition of glass. The cluster group scatter plot is then plotted and the subclass results are highly reasonable. Then a stepwise regression model is established to analyze the classification sensitivity and obtain several indicators with high sensitivity, which can be used for more targeted protection and restoration of unearthed cultural relics.

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Wang, Yuchen, Wenkai Lu, and Benfeng Wang. "An Events Rearrangement Strategy-Based Robust Principle Component Analysis." IEEE Geoscience and Remote Sensing Letters 14, no. 6 (June 2017): 881–85. http://dx.doi.org/10.1109/lgrs.2017.2685631.

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Murinto, Murinto. "PENGENALAN WAJAH MANUSIA DENGAN METODE PRINCIPLE COMPONENT ANALYSIS (PCA)." TELKOMNIKA (Telecommunication Computing Electronics and Control) 5, no. 3 (December 1, 2007): 177. http://dx.doi.org/10.12928/telkomnika.v5i3.1364.

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35

Fiori, Simone. "Neural independent component analysis by ‘maximum-mismatch’ learning principle." Neural Networks 16, no. 8 (October 2003): 1201–21. http://dx.doi.org/10.1016/s0893-6080(03)00057-1.

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36

Sivasathya, M., and S. Mary Joans. "Image Feature Extraction using Non Linear Principle Component Analysis." Procedia Engineering 38 (2012): 911–17. http://dx.doi.org/10.1016/j.proeng.2012.06.114.

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37

Gai, Yu Chun, Wei Dong Zhu, Ying Lin Ke, Xiao Hong Yin, Jun Hao Wu, and Wei Wang. "Locating Error Analysis for Cartesian Positioner." Applied Mechanics and Materials 419 (October 2013): 157–62. http://dx.doi.org/10.4028/www.scientific.net/amm.419.157.

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The structure and the operating principle of a Cartesian positioner are expounded. Based on the principle of multi-body system dynamics, the kinematic model of the Cartesian positioner is established. The deformation of each component is analyzed during the fuselage pose alignment by FEA(finite element analysis) . At last the locating error model is established. The locating error model can contribute to the stiffness distribution of components and the structure optimization of the Cartesian positioner.

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Fitriani, Fitriani, Sudiyo Sudiyo, Dayang Berliana, and Evi Yuniarti. "Primary components in shaping koperasi masjid’s participation level: Employing a principle component analysis approach." Masyarakat, Kebudayaan dan Politik 31, no. 3 (September 29, 2018): 318. http://dx.doi.org/10.20473/mkp.v31i32018.318-327.

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Koperasi masjid present themselves as a part of muamalah through Sharia economic facilitation. This study was conducted to analyse the cooperative members’ participation level in relation to the implementation of a cooperative business program. A survey using a case study approach was conducted at koperasi Masjid Hafshotul Iman, Masjid Taqwa and Masjid Darul HikmahThe survey employed a purposive sampling method with the participation of the cooperative’s management (chairman, secretary, and treasurer) and the masjid’s members. Ten respondents were purposively chosen from each mosque and a total 30 respondents were acquired. The distributed questionnaire was a closed one, measured with a Likert scale. The results were analysed using factor analysis and the Principal Component Analysis approach (PCA). Based on the results of the factor analysis, the members’ participation level in cooperative service can be classified into three primary factors. Some of the variables displayed a strong correlation with Factor 1, namely motivation, management performance, RAT performance, and supervisor performance. The second primary factor was formed by activeness in participating in cooperative activities. Lastly, the third primary factor encompassed three variables with a strong correlation, namely age, education, and membership duration.

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A. M., Mohamed,, Abdel Latif, S. H, and Alwan, A. S. "Support vector regression is an improvement for principle component analysis." Journal of University of Shanghai for Science and Technology 23, no. 06 (June 26, 2021): 1699–715. http://dx.doi.org/10.51201/jusst/21/06462.

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The principle component analysis is used more frequently as a variables reduction technique. And recently, an evolving group of studies makes use of machine learning regression algorithms to improve the estimation of empirical models. One of the most frequently used machines learning regression models is support vector regression with various kernel functions. However, an ensemble of support vector regression and principal component analysis is also possible. So, this paper aims to investigate the competence of support vector regression techniques after performing principal component analysis to explore the possibility of reducing data and having more accurate estimations. Some new proposals are introduced and the behavior of two different models 𝜀𝜀-SVR and 𝑣𝑣-SVR are compared through an extensive simulation study under four different kernel functions; linear, radial, polynomial, and sigmoid kernel functions, with different sample sizes, ranges from small, moderate to large. The models are compared with their counterparts in terms of coefficient of determination (𝑅𝑅2 ) and root mean squared error (RMSE). The comparative results show that applying SVR after PCA models improve the results in terms of SV numbers between 30% and 60% on average and it can be applied with real data. In addition, the linear kernel function gave the best values rather than other kernel functions and the sigmoid kernel gave the worst values. Under 𝜀𝜀-SVR the results improved which did not happen with 𝑣𝑣-SVR. It is also drawn that, RMSE values decreased with increasing sample size.

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Liu, Guowei, Fengshan Ma, Gang Liu, Haijun Zhao, Jie Guo, and Jiayuan Cao. "Application of Multivariate Statistical Analysis to Identify Water Sources in A Coastal Gold Mine, Shandong, China." Sustainability 11, no. 12 (June 17, 2019): 3345. http://dx.doi.org/10.3390/su11123345.

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Submarine mine water inrush has become a problem that must be urgently solved in coastal gold mining operations in Shandong, China. Research on water in subway systems introduced classifications for the types of mine groundwater and then established the functions used to identify each type of water sample. We analyzed 31 water samples from −375 m underground using multivariate statistical analysis methods. Cluster analysis combined with principle component analysis and factor analysis divided water samples into two types, with one type being near the F3 fault. Principal component analysis identified four principle components accounting for 91.79% of the total variation. These four principle components represented almost all the information about the water samples, which were then used as clustering variables. A Bayes model created by discriminant analysis demonstrated that water samples could also be divided into two types, which was consistent with the cluster analysis result. The type of water samples could be determined by placing Na+ and CHO3− concentrations of water samples into Bayes functions. The results demonstrated that F3, which is a regional fault and runs across the whole Xishan gold mine, may be the potential channel for water inrush, providing valuable information for predicting the possibility of water inrush and thus reducing the costs of the mining operation.

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Maharnisha, Gandla, Gandla Roopesh Kumar, and R. Arunraj. "Satellite Image Registration and Image Fusion by using Principle Component Analysis." International Journal of Engineering & Technology 7, no. 2.19 (April 17, 2018): 106. http://dx.doi.org/10.14419/ijet.v7i2.19.15063.

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This aims to fused image registration and image fusion used to spatial resolution images by principle component analysis method. Digital image processing requires either the full image or a part of image. It will be processed from the user’s point of view like the radius of object. Wavelet technique will improve the spatial resolution to produce spectral degradation in output image. To overcome the spectral degradation, PCA fusion method can be used. PCA uses curve which represent edges and extraction of the detailed information from the image.PAN and MS images are used by individual acquired low frequency approximate component and high frequency detail components in this PCA. To evaluate the image fusion accuracy, Peak Signal to Noise Ratio and Root Mean Square Error are used. The advantages of using digital image processing are preservation of original data accuracy, flexibility and repeatability.

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Hou Yuanyuan, 侯园园, and 徐建程 Xu Jiancheng. "Random Phase-Shifting Speckle Patterns Analysis Algorithm Based on Principle Component Analysis." Chinese Journal of Lasers 43, no. 12 (2016): 1204002. http://dx.doi.org/10.3788/cjl201643.1204002.

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Danklmayer, A., M. Chandra, and E. Lüneburg. "Principal Component Analysis In Radar Polarimetry." Advances in Radio Science 3 (May 13, 2005): 399–400. http://dx.doi.org/10.5194/ars-3-399-2005.

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Abstract. Second order moments of multivariate (often Gaussian) joint probability density functions can be described by the covariance or normalised correlation matrices or by the Kennaugh matrix (Kronecker matrix). In Radar Polarimetry the application of the covariance matrix is known as target decomposition theory, which is a special application of the extremely versatile Principle Component Analysis (PCA). The basic idea of PCA is to convert a data set, consisting of correlated random variables into a new set of uncorrelated variables and order the new variables according to the value of their variances. It is important to stress that uncorrelatedness does not necessarily mean independent which is used in the much stronger concept of Independent Component Analysis (ICA). Both concepts agree for multivariate Gaussian distribution functions, representing the most random and least structured distribution. In this contribution, we propose a new approach in applying the concept of PCA to Radar Polarimetry. Therefore, new uncorrelated random variables will be introduced by means of linear transformations with well determined loading coefficients. This in turn, will allow the decomposition of the original random backscattering target variables into three point targets with new random uncorrelated variables whose variances agree with the eigenvalues of the covariance matrix. This allows a new interpretation of existing decomposition theorems.

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KAKUMA, Toshiaki. "The Business Consultation on Pig Farming by Principle Component Analysis." Nihon Yoton Gakkaishi 39, no. 4 (2002): 255–63. http://dx.doi.org/10.5938/youton.39.255.

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Zhou, Jing, Haobo Qi, Yu Chen, and Hansheng Wang. "Progressive principle component analysis for compressing deep convolutional neural networks." Neurocomputing 440 (June 2021): 197–206. http://dx.doi.org/10.1016/j.neucom.2021.01.035.

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ZHANG, YAN, BIN YU, and HAI-MING GU. "FACE RECOGNITION USING CURVELET-BASED TWO-DIMENSIONAL PRINCIPLE COMPONENT ANALYSIS." International Journal of Pattern Recognition and Artificial Intelligence 26, no. 03 (May 2012): 1256009. http://dx.doi.org/10.1142/s0218001412560095.

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The task of face recognition has been actively researched in recent years because of its many applications in various domains. This paper presents a robust face recognition system using curvelet-based two-dimensional principle component analysis (2D PCA) to address the problem of human face recognition from still images. 2D PCA has advantages over PCA in evaluating the covariance matrix accurately and time complexity. Inspired by the attractive attributes of curvelets in catching the edge singularities with very few coefficients in a non-adaptive manner, we introduce the scheme of decomposing images into curvelet subbands and applying 2D PCA to create a representative feature set. Experiments were designed with different implementations of each module using standard testing database. We experimented with changing the illumination normalization procedure; comparing the baseline PCA-based method with the proposed scheme; studying effects on algorithm performance of k-nearest neighbor (kNN) classifier and Support Vector Machine (SVM) classifier in the classification process; also we experimented with different databases such as FERET, etc. High accuracy rate were achieved by the proposed scheme through a comparative study.

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N.Pai, Appurai, and Smt T. Jayakumari. "Feature Subset Selection Techniques - Swift Clustering and Principle Component Analysis." International Journal of Computer Trends and Technology 11, no. 2 (May 25, 2014): 89–93. http://dx.doi.org/10.14445/22312803/ijctt-v11p119.

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Wang, Qiang, and Zhengzhi Wang. "Compact local color descriptor based on quaternion principle component analysis." Journal of Electronic Imaging 22, no. 3 (August 30, 2013): 033021. http://dx.doi.org/10.1117/1.jei.22.3.033021.

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Saha, Ashim, and Sambhu Nath Pradhan. "Facial expression recognition based on eigenspaces and principle component analysis." International Journal of Computational Vision and Robotics 8, no. 2 (2018): 190. http://dx.doi.org/10.1504/ijcvr.2018.091980.

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Saha, Ashim, and Sambhu Nath Pradhan. "Facial expression recognition based on eigenspaces and principle component analysis." International Journal of Computational Vision and Robotics 8, no. 2 (2018): 190. http://dx.doi.org/10.1504/ijcvr.2018.10013159.

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