Academic literature on the topic 'Principle component analysis'
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Journal articles on the topic "Principle component analysis"
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.
Full textPolyak, 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.
Full textChen, 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.
Full textWu, 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.
Full textArab, 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.
Full textKim, 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.
Full textDholakia, 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.
Full textZhang, 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.
Full textBowden, 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.
Full textPurwandari, 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.
Full textDissertations / Theses on the topic "Principle component analysis"
Chen, Huanting. "Portfolio Construction Using Principle Component Analysis." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-theses/927.
Full textShawli, 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.
Full textLa version abrégée du questionnaire SF-36 est largement utilisée pour valider la qualité de vie reliée à la santé. Ce questionnaire fournit huit scores s'attardant à la capacité fonctionnelle et au bien-être, lesquels sont regroupés en cotes sommaires attribuées aux composantes physiques et mentales. Cependant, des études récentes ont rapporté des résultats contradictoires entre les huit sous-échelles et les deux cotes sommaires lorsque les scores sont obtenus auprès de sujets malades. Cette discordance serait due à la méthode utilisée pour calculer les cotes sommaires du SF-36 qui est fondée sur l'analyse en composantes principales avec rotation orthogonale.Dans cette thèse, nous explorons diverses méthodes dans le but d'identifier une méthode plus précise pour calculer les cotes sommaires du SF-36 attribuées aux composantes physiques et mentales (CCP et CCM), en mettant l'accent sur des sous-populations de sujets malades. Nous évaluerons d'abord des méthodes traditionnelles d'analyse de données, telles que l'analyse en composantes principales (ACP) et l'analyse factorielle, en utilisant l'étude de l'estimation du maximum de vraisemblance et en appliquant les rotations orthogonale et oblique aux deux méthodes sur les données du registre du Groupe de recherche canadien sur la sclérodermie. Nous comparons ces approches courantes à une méthode d'analyse de données développée récemment à partir de travaux de recherche sur le réseau neuronal et le traitement du signal, l'analyse en composantes indépendantes (ACI).Nous avons découvert que la rotation oblique est la seule méthode qui réduit les cotes attribuées aux composantes mentales moyennes afin de mieux les corréler aux scores de la sous-échelle des symptômes mentaux. Dans le but de mieux comprendre les différences entre la rotation orthogonale et la rotation oblique, nous avons étudié le rendement de l'ACP avec deux approches pour déterminer les véritables cotes sommaires attribuées aux composantes physiques et mentales dans une population simulée de sujets malades pour laquelle les données étaient connues. Nous avons exploré les méthodes dans des situations où les scores véritables étaient indépendants et lorsqu'ils étaient corrélés. Nous avons conclu que le rendement de l'ACI et de l'ACP associées à la rotation orthogonale était très similaire lorsque les données étaient indépendantes, mais que le rendement différait lorsque les données étaient corrélées (ACI étant moins performante). L'ACP associée à la rotation oblique a tendance à être moins performante que les deux méthodes lorsque les données étaient indépendantes, mais elle est plus performante lorsque les données étaient corrélées. Nous discutons également du lien entre l'ACI et l'ACP avec la rotation orthogonale, ce qui appuie l'emploi de la rotation varimax dans le questionnaire SF 36.Enfin, nous avons appliqué l'ACI aux données sur la sclérodermie et nous avons mis en évidence une corrélation relativement faible entre l'ACI et l'ACP sans rotation dans l'estimation des scores CCP et CCM, et une corrélation très élevée entre l'ACI et l'ACP avec rotation varimax. L'ACP avec rotation oblique présentait également une corrélation relativement élevée avec l'ACI. Par conséquent, nous en avons conclu que l'ACI pourrait servir de solution de compromis entre ces deux méthodes.
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.
Full textHolm, 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.
Full textMahmood, 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.
Full textSIPL, Mechatronics, GIST 1 Oryong-Dong, Buk-Gu, Gwangju, 500-712 South Korea tel. 0082-62-970-2997
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.
Full textLi, 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.
Full textChemistruck, 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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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.
Full textIn this thesis we develop models and algorithms based on the slowness principle in the auditory domain. Several experimental results as well as the successful results in the visual domain indicate that, despite the different nature of the sensory signals, the slowness principle may play an important role in the auditory domain as well, if not in the cortex as a whole. Different modeling approaches have been used, which make use of several alternative representations of the auditory stimuli. We show the limitations of these approaches. In the domain of signal processing, the slowness principle and its straightforward implementation, the Slow Feature Analysis algorithm, has been proven to be useful beyond biologically inspired modeling. A novel algorithm for nonlinear blind source separation is described that is based on a combination of the slowness and the statistical independence principles, and is evaluated on artificial and real-world audio signals. The Modular toolkit for Data Processing open source software library is additionally presented.
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.
Full textBooks on the topic "Principle component analysis"
Jolliffe, I. T. Principal component analysis. 2nd ed. New York: Springer, 2010.
Find full textHyvarinen, Aapo. Independent component analysis. New York: J. Wiley, 2001.
Find full textJuha, Karhunen, and Oja Erkki, eds. Independent component analysis. New York: J. Wiley, 2001.
Find full textA user's guide to principal components. Hoboken, N.J: Wiley-Interscience, 2003.
Find full textFlury, Bernhard. Commonprincipal components and related multivariate models. New York: Wiley, 1988.
Find full textJackson, J. Edward. A user's guide to principal components. New York: Wiley, 1991.
Find full textFlury, Bernhard. Common principal components and related multivariate models. New York: Wiley, 1988.
Find full textPrincipal component analysis. New York: Springer-Verlag, 1986.
Find full textPrincipal component analysis. 2nd ed. New York: Springer, 2002.
Find full textJolliffe, I. T. Principal Component Analysis. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4757-1904-8.
Full textBook chapters on the topic "Principle component analysis"
Deutsch, Hans-Peter. "Principle Component Analysis." In Derivatives and Internal Models, 539–47. London: Palgrave Macmillan UK, 2002. http://dx.doi.org/10.1057/9780230502109_32.
Full textDeutsch, Hans-Peter. "Principle Component Analysis." In Derivatives and Internal Models, 615–23. London: Palgrave Macmillan UK, 2004. http://dx.doi.org/10.1057/9781403946089_35.
Full textKinser, Jason M. "Principle Component Analysis." In Image Operators, 111–26. First edition. | Boca Raton, FL: CRC Press/Taylor & Francis Group, [2019] |: CRC Press, 2018. http://dx.doi.org/10.1201/9780429451188-8.
Full textLai, Zhihui, Mangqi Chen, Dongmei Mo, Xingxing Zou, and Heng Kong. "Sparse Discriminant Principle Component Analysis." In Artificial Intelligence on Fashion and Textiles, 111–19. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99695-0_14.
Full textItoh, Hayato, Atsushi Imiya, and Tomoya Sakai. "Low-Dimensional Tensor Principle Component Analysis." In Computer Analysis of Images and Patterns, 715–26. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23192-1_60.
Full textGatignon, Hubert. "Reliability Alpha, Principle Component Analysis, and Exploratory Factor Analysis." In Statistical Analysis of Management Data, 29–57. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-1270-1_3.
Full textMatsuda, Yoshitatsu, and Kazunori Yamaguchi. "The InfoMin Principle for ICA and Topographic Mappings." In Independent Component Analysis and Blind Signal Separation, 958–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11679363_119.
Full textKumar, A. Pavan, Sukhendu Das, and V. Kamakoti. "Face Recognition Using Weighted Modular Principle Component Analysis." In Neural Information Processing, 362–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30499-9_55.
Full textJamshidi, Mo, Barney Tannahill, and Arezou Moussavi. "Big Data Analytic Paradigms: From Principle Component Analysis to Deep Learning." In Robust Intelligence and Trust in Autonomous Systems, 79–95. Boston, MA: Springer US, 2016. http://dx.doi.org/10.1007/978-1-4899-7668-0_5.
Full textChen, Qingzhang, Weiyi Zhang, Xiaoying Chen, and Jianghong Han. "A Facial Expression Classification Algorithm Based on Principle Component Analysis." In Advances in Neural Networks - ISNN 2006, 55–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11760023_9.
Full textConference papers on the topic "Principle component analysis"
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.
Full textHaenselmann, 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.
Full textBreesam, 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.
Full textCai, 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.
Full textChen, 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.
Full textTang, 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.
Full textYu, 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.
Full textJingbo 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.
Full textXiao, 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.
Full textGuo, 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.
Full textReports on the topic "Principle component analysis"
Haghighi, F. Kernel Principle Component Analysis of Microarray Data. Final Report. Office of Scientific and Technical Information (OSTI), November 2003. http://dx.doi.org/10.2172/823317.
Full textMARTIN, SHAWN B. Kernel Near Principal Component Analysis. Office of Scientific and Technical Information (OSTI), July 2002. http://dx.doi.org/10.2172/810934.
Full textAït-Sahalia, Yacine, and Dacheng Xiu. Principal Component Analysis of High Frequency Data. Cambridge, MA: National Bureau of Economic Research, September 2015. http://dx.doi.org/10.3386/w21584.
Full textEick, Brian, Zachary Treece, Billie Spencer, Matthew Smith, Steven Sweeney, Quincy Alexander, and Stuart Foltz. Miter gate gap detection using principal component analysis. Engineer Research and Development Center (U.S.), June 2018. http://dx.doi.org/10.21079/11681/27365.
Full textFederer, W. T., C. E. McCulloch, and J. J. Miles-McDermott. Illustrative Examples of Principal Component Analysis Using SYSTAT/FACTOR. Fort Belvoir, VA: Defense Technical Information Center, May 1987. http://dx.doi.org/10.21236/ada184920.
Full textFederer, W. T., C. E. McCulloch, and N. J. Miles-McDermott. Illustrative Examples of Principal Component Analysis using BMDP/4M. Fort Belvoir, VA: Defense Technical Information Center, May 1987. http://dx.doi.org/10.21236/ada185179.
Full textKrishnaiah, P. R., and S. Sarkar. Principal Component Analysis Under Correlated Multivariate Regression Equations Model. Fort Belvoir, VA: Defense Technical Information Center, April 1985. http://dx.doi.org/10.21236/ada160266.
Full textThompson, David C., Janine C. Bennett, Diana C. Roe, and Philippe Pierre Pebay. Scalable multi-correlative statistics and principal component analysis with Titan. Office of Scientific and Technical Information (OSTI), February 2009. http://dx.doi.org/10.2172/984172.
Full textFujikoshi, Y., P. R. Krishnaiah, and J. Schmidhammer. Effect of Additional Variables in Principal Component Analysis, Discriminant Analysis and Canonical Correlation Analysis. Fort Belvoir, VA: Defense Technical Information Center, August 1985. http://dx.doi.org/10.21236/ada162069.
Full textHarter, Rachel M., Pinliang (Patrick) Chen, Joseph P. McMichael, Edgardo S. Cureg, Samson A. Adeshiyan, and Katherine B. Morton. Constructing Strata of Primary Sampling Units for the Residential Energy Consumption Survey. RTI Press, May 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0041.1705.
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