Relevant bibliographies by topics / Principle component analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Principle component analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Principle component analysis"

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

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

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

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Wu, Danyang, Han Zhang, Feiping Nie, et al. "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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Arab, Abbas, Jamila Harbi, and Amel Abbas. "Image Compression Using Principle Component Analysis." Al-Mustansiriyah Journal of Science 29, no. 2 (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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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
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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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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 (2013): 181–90. http://dx.doi.org/10.14257/ijbsbt.2013.5.6.19.

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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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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 (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 s
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Dissertations / Theses on the topic "Principle component analysis"

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Chen, Huanting. "Portfolio Construction Using Principle Component Analysis." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-theses/927.

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"Principal Components Analysis (PCA) is an important mathematical technique widely used in the world of quantitative finance. The ultimate goal of this paper is to construct a portfolio with hedging positions, which is able to outperform the SPY benchmark in terms of the Sharpe ratio. Mathematical techniques implemented in this paper besides principle component analysis are the Sharpe ratio, ARMA, ARCH, GARCH, ACF, and Markowitz methodology. Information about these mathematical techniques is listed in the introduction section. Through conducting in sample analysis, out sample analysis, and bac
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Shawli, Alaa. "Scoring the SF-36 health survey in scleroderma using independent component analysis and principle component analysis." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=97180.

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The short form SF-36 survey is a widely used survey of patient health related quality of life. It yields eight subscale scores of functional health and well-being that are summarized by two physical and mental component summary scores. However, recent studies have reported inconsistent results between the eight subscales and the two component summary measures when the scores are from a sick population. They claim that this problem is due to the method used to compute the SF-36 component summary scores, which is based on principal component analysis with orthogonal rotation.In this thesis, we e
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Yaseen, Muhammad Usman. "Identification of cause of impairment in spiral drawings, using non-stationary feature extraction approach." Thesis, Högskolan Dalarna, Datateknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:du-6473.

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Parkinson’s disease is a clinical syndrome manifesting with slowness and instability. As it is a progressive disease with varying symptoms, repeated assessments are necessary to determine the outcome of treatment changes in the patient. In the recent past, a computer-based method was developed to rate impairment in spiral drawings. The downside of this method is that it cannot separate the bradykinetic and dyskinetic spiral drawings. This work intends to construct the computer method which can overcome this weakness by using the Hilbert-Huang Transform (HHT) of tangential velocity. The work is
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Holm, Klaus Herman. "Assessment of Atlanta’s PM [subscript 2.5] source profiles using principle component analysis and positive matrix factorization." Thesis, Georgia Institute of Technology, 2002. http://hdl.handle.net/1853/20751.

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Mahmood, Muhammad Tariq. "Face Detection by Image Discriminating." Thesis, Blekinge Tekniska Högskola, Avdelningen för för interaktion och systemdesign, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4352.

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Human face recognition systems have gained a considerable attention during last few years. There are very many applications with respect to security, sensitivity and secrecy. Face detection is the most important and first step of recognition system. Human face is non rigid and has very many variations regarding image conditions, size, resolution, poses and rotation. Its accurate and robust detection has been a challenge for the researcher. A number of methods and techniques are proposed but due to a huge number of variations no one technique is much successful for all kinds of faces and images
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Chisholm, Daniel J. "Use of Principle Component Analysis for the identification and mapping of phases from energy-dispersive x-ray spectra." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1999. http://handle.dtic.mil/100.2/ADA359572.

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Li, Yancan. "The Effects of Ownership on Bank Performance: A Study of Commercial Banks in China." Scholarship @ Claremont, 2012. http://scholarship.claremont.edu/cmc_theses/515.

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Many Chinese commercial banks have experienced ownership transitions during the past decade, along with significant improvements in performance. In order to examine the effect of ownership on bank performance, an empirical study of Chinese commercial banks is performed. A dataset covering 16 Chinese commercial banks over the period of 2002 — 2011 is tested using linear regression model and principle component analysis. It is found that being a Joint-Stock Commercial Bank has a positive effect on earnings per share (EPS), and being a City Commercial Bank increases return on assets (ROA). On the
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Chemistruck, Heather Michelle. "A Galerkin Approach to Define Measured Terrain Surfaces with Analytic Basis Vectors to Produce a Compact Representation." Diss., Virginia Tech, 2010. http://hdl.handle.net/10919/29585.

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The concept of simulation-based engineering has been embraced by virtually every research and industry sector (Sinha, Liang et al. 2001; Mocko and Fenves 2003). Engineering and science communities have become increasingly aware that computer simulation is an indispensable tool for resolving a multitude of scientific and technological problems. It is clearly desirable to gain a reliable perspective on the behaviour of a system early in the design stage, long before building costly prototypes (Chul and Ro 2002; Letherwood, Gunter et al. 2004; Makarand Datar 2007; Ersal, Fathy et al. 2008; Muel
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Zito, Tiziano. "Exploring the slowness principle in the auditory domain." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2012. http://dx.doi.org/10.18452/16450.

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In dieser Arbeit werden - basierend auf dem Langsamkeitsprinzip - Modelle und Algorithmen für das auditorische System entwickelt. Verschiedene experimentelle Ergebnisse, sowie die erfolgreichen Ergebnisse im visuellen System legen nahe, dass, trotz der unterschiedlichen Beschaffenheit visueller und auditorischer sensorischer Signale, das Langsamkeitsprinzip auch im auditorischen System eine bedeutsame Rolle spielen könnte, und vielleicht auch im Kortex im Allgemeinen. Es wurden verschiedene Modelle für unterschiedliche Repräsentationen des auditorischen Inputs realisiert. Es werden die B
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Bloxson, Julie M. "Characterization of the Porosity Distribution within the Clinton Formation, Ashtabula County, Ohio by Geophysical Core and Well Logging." Kent State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=kent1341879463.

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Books on the topic "Principle component analysis"

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Jolliffe, I. T. Principal component analysis. 2nd ed. Springer, 2010.

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Hyvarinen, Aapo. Independent component analysis. J. Wiley, 2001.

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Juha, Karhunen, and Oja Erkki, eds. Independent component analysis. J. Wiley, 2001.

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Dunteman, George H. Principal components analysis. Sage Publications, 1989.

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Dunteman, George H. Principal components analysis. Sage Publications, 1989.

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Flury, Bernhard. Commonprincipal components and related multivariate models. Wiley, 1988.

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Jackson, J. Edward. A user's guide to principal components. Wiley, 1991.

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Flury, Bernhard. Common principal components and related multivariate models. Wiley, 1988.

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Jolliffe, I. T. Principal Component Analysis. Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4757-1904-8.

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Dunteman, George. Principal Components Analysis. SAGE Publications, Inc., 1989. http://dx.doi.org/10.4135/9781412985475.

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Book chapters on the topic "Principle component analysis"

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Kinser, Jason M. "Principle Component Analysis." In Image Operators. CRC Press, 2018. http://dx.doi.org/10.1201/9780429451188-8.

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Deutsch, Hans-Peter. "Principle Component Analysis." In Derivatives and Internal Models. Palgrave Macmillan UK, 2004. http://dx.doi.org/10.1057/9781403946089_35.

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Deutsch, Hans-Peter. "Principle Component Analysis." In Derivatives and Internal Models. Palgrave Macmillan UK, 2002. http://dx.doi.org/10.1057/9780230502109_32.

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Lai, Zhihui, Mangqi Chen, Dongmei Mo, Xingxing Zou, and Heng Kong. "Sparse Discriminant Principle Component Analysis." In Artificial Intelligence on Fashion and Textiles. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99695-0_14.

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Harinakshi, C., Ananya Shetty, Sapna G. Acharya, and Vignesh V. Vageesh. "River water principle component analysis." In Sustainable Materials, Structures and IoT. CRC Press, 2024. http://dx.doi.org/10.1201/9781003596776-22.

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Itoh, Hayato, Atsushi Imiya, and Tomoya Sakai. "Low-Dimensional Tensor Principle Component Analysis." In Computer Analysis of Images and Patterns. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23192-1_60.

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Gatignon, Hubert. "Reliability Alpha, Principle Component Analysis, and Exploratory Factor Analysis." In Statistical Analysis of Management Data. Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-1270-1_3.

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Matsuda, Yoshitatsu, and Kazunori Yamaguchi. "The InfoMin Principle for ICA and Topographic Mappings." In Independent Component Analysis and Blind Signal Separation. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11679363_119.

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Kumar, A. Pavan, Sukhendu Das, and V. Kamakoti. "Face Recognition Using Weighted Modular Principle Component Analysis." In Neural Information Processing. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30499-9_55.

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Yang, Lan, Bin Zhang, Qingrong Feng, Xinling Liu, and Jianjun Wang. "Schatten Capped p Regularization for Robust Principle Component Analysis." In Advances in Computer Graphics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-50078-7_3.

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Conference papers on the topic "Principle component analysis"

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Xu Chunming, Jiang Haibo, and Yu Jianjiang. "Robust two-dimensional principle component analysis." In 2008 Chinese Control Conference (CCC). IEEE, 2008. http://dx.doi.org/10.1109/chicc.2008.4605066.

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Haenselmann, Thomas, and Wolfgang Effelsberg. "Texture resynthesis using principle component analysis." In Electronic Imaging 2002, edited by Bernice E. Rogowitz and Thrasyvoulos N. Pappas. SPIE, 2002. http://dx.doi.org/10.1117/12.469543.

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Breesam, Aqeel M., Mousa K. Wail, and Rashid A. Fayadh. "Principle component analysis based face recognition." In PROCEEDING OF THE 1ST INTERNATIONAL CONFERENCE ON ADVANCED RESEARCH IN PURE AND APPLIED SCIENCE (ICARPAS2021): Third Annual Conference of Al-Muthanna University/College of Science. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0093821.

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Cai, T. Tony, Zongming Ma, and Yihong Wu. "Recent results on sparse principle component analysis." In 2013 IEEE 5th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2013. http://dx.doi.org/10.1109/camsap.2013.6714037.

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Chen, Hsin-Ting, Hsuan Ren, and Yang-Lang Chang. "Greedy modular subspace segment principle component analysis." In Optics East 2007, edited by Kenneth J. Ewing, James B. Gillespie, Pamela M. Chu, and William J. Marinelli. SPIE, 2007. http://dx.doi.org/10.1117/12.739230.

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Tang, Xiaoou, and William K. Stewart. "Texture classification using principle-component analysis techniques." In Satellite Remote Sensing, edited by Jacky Desachy. SPIE, 1994. http://dx.doi.org/10.1117/12.196722.

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Yu, Chia-Mu, and Li-Wei Kang. "Sensor localization via robust principle component analysis." In 2014 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW). IEEE, 2014. http://dx.doi.org/10.1109/icce-tw.2014.6904065.

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Jingbo Zhou, Zhong Jin, and Jingyu Yang. "Multiscale saliency detection using principle component analysis." In 2012 International Joint Conference on Neural Networks (IJCNN 2012 - Brisbane). IEEE, 2012. http://dx.doi.org/10.1109/ijcnn.2012.6252566.

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Xiao, Xiaolin, and Yicong Zhou. "Two-Dimensional Quaternion Sparse Principle Component Analysis." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8462668.

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Guo, Zhi-bo, and Yun-yang Yan. "A Retrieve Space Principal Component Analysis Based on the Image Retrieve Principle." In 2009 Chinese Conference on Pattern Recognition (CCPR). IEEE, 2009. http://dx.doi.org/10.1109/ccpr.2009.5344154.

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Reports on the topic "Principle component analysis"

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Haghighi, F. Kernel Principle Component Analysis of Microarray Data. Final Report. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/823317.

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เอี่ยมกาญจนาลัย, สมเกียรติ, ณัฐพล อัสสะรัตน์ та สุวาณี สุรเสียงสังข์. ผลกระทบของค่านิยมและวิถีชีวิตต่อทัศนคติต่อผู้สูงอายุและปฏิสัมพันธ์ในสังคม กรณีศึกษาในเขตกรุงเทพมหานคร : รายงานการวิจัย. จุฬาลงกรณ์มหาวิทยาลัย, 2015. https://doi.org/10.58837/chula.res.2015.21.

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งานวิจัยนี้มีวัตถุประสงค์เพื่อจำแนกประชากรกรุงเทพมหานครอายุระหว่าง 18-59 ปี ตามปัจจัยวิถีชีวิตและค่านิยม และศึกษาทัศนคติและการมีปฏิสัมพันธ์กับผู้สูงอายุ โดยการสุ่มตัวอย่างแบบโควต้าตาม เพศ และกลุ่มอายุ ขนาด 1,020 คน เก็บข้อมูลโดยใช้แบบสอบถามที่มีคำถามเกี่ยวกับวิถีชีวิต ค่านิยม คุณลักษณะทางประชากรศาสตร์ ทัศนคติต่อผู้สูงอายุ และการมีปฏิสัมพันธ์ การวิเคราะห์องค์ประกอบของปัจจัยด้านวิถีชีวิต ค่านิยม และทัศนคติต่อผู้สูงอายุ ใช้เทคนิคการวิเคราะห์องค์ประกอบหลัก (Principle Component Analysis) สามารถกำหนดโครงสร้างของปัจจัยด้านวิถีชีวิตได้ 7 ปัจจัย ปัจจัยด้านค่านิยมได้ 5 ปัจจัย และปัจจัยด้านทัศนคติต่อผู้ส
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MARTIN, SHAWN B. Kernel Near Principal Component Analysis. Office of Scientific and Technical Information (OSTI), 2002. http://dx.doi.org/10.2172/810934.

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Hamilton, James, and Jin Xi. Principal Component Analysis for Nonstationary Series. National Bureau of Economic Research, 2024. http://dx.doi.org/10.3386/w32068.

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Ayres, João, Arturo Galindo, Santiago Novoa, and Victoria Nuguer. Inflation Dynamics in Latin America and the Caribbean. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0004751.

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We perform a principal component analysis of the inflation dynamics in Latin America and the Caribbean to assess the recent surge in inflation across the region. The principal component accounts for 57% of the variation in inflation in the last 17 years, and it is highly correlated to the principal components of inflation of country groups outside the region, especially post-COVID-19. Global factors such as US inflation, commodity prices, and international shipping costs can account for at least one-third of the variation of the principal component. The analysis implies that external factors a
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Aït-Sahalia, Yacine, and Dacheng Xiu. Principal Component Analysis of High Frequency Data. National Bureau of Economic Research, 2015. http://dx.doi.org/10.3386/w21584.

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Zhu, Yifan. Principal Components Analysis of Discrete Datasets. Iowa State University, 2018. http://dx.doi.org/10.31274/cc-20240624-1152.

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Eick, Brian, Zachary Treece, Billie Spencer, et al. Miter gate gap detection using principal component analysis. Engineer Research and Development Center (U.S.), 2018. http://dx.doi.org/10.21079/11681/27365.

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Federer, W. T., C. E. McCulloch, and J. J. Miles-McDermott. Illustrative Examples of Principal Component Analysis Using SYSTAT/FACTOR. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada184920.

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Federer, W. T., C. E. McCulloch, and N. J. Miles-McDermott. Illustrative Examples of Principal Component Analysis using BMDP/4M. Defense Technical Information Center, 1987. http://dx.doi.org/10.21236/ada185179.

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