Relevant bibliographies by topics / Principle component analysis

Academic literature on the topic 'Principle component analysis'

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Published: 4 June 2021
Last updated: 31 January 2023

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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 (January 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 (March 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 (May 2004): 1081–83. http://dx.doi.org/10.1016/j.patcog.2003.09.004.

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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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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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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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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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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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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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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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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 back testing, it is demonstrated that the quantitative approach adopted in this paper, such as principle component analysis, can be used to find the major driving factor causing movements of a portfolio, and we can perform a more effective portfolio analysis by using principle component analysis to reduce the dimensions of a financial model."
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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 explore various methods in order to identify a method that is more accurate in obtaining the SF-36 physical and mental component component summary scores (PCS and MCS), with a focus on diseased patient subpopulations. We first explore traditional data analysis methods such as principal component analysis (PCA) and factor analysis using maximum likelihoodestimation and apply orthogonal and oblique rotations with both methods to data from the Canadian Scleroderma Research Group registry. We compare these common approaches to a recently developed data analysis method from signal processing and neural network research, independent component analysis (ICA). We found that oblique rotation is the only method that reduces the meanmental component scores to best match the mental subscale scores. In order to try to better elucidate the differences between the orthogonal and oblique rotation, we studied the performance of PCA with the two approaches for recovering the true physical and mental component summary scores in a simulated diseased population where we knew the truth. We explored the methods in situations where the true scores were independent and when they were also correlated. We found that ICA and PCA with orthogonal rotation performed very similarly when the data were generated to be independent, but differently (with ICA performing worse) when the data were generated to be correlated. PCA with oblique rotation tended to perform worse than both methods when the data were independent, but better when the data were correlated. We also discuss the connection between ICA and PCA with orthogonal rotation, which lends strength to the use of the varimax rotation for the SF-36.Finally, we applied ICA to the scleroderma data and found relatively low correlation between ICA and unrotated PCA in estimating the PCS and MCS scores and very high correlation between ICA and PCA with varimax rotation. PCA with oblique rotation also had a relatively high correlation with ICA. Hence, we concluded that ICA could be seen as a compromise solution between the two methods.
La 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.
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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 done under supervised learning, so a target class is used which is acquired from a neurologist using a web interface. After reducing the dimension of HHT features by using PCA, classification is performed. C4.5 classifier is used to perform the classification. Results of the classification are close to random guessing which shows that the computer method is unsuccessful in assessing the cause of drawing impairment in spirals when evaluated against human ratings. One promising reason is that there is no difference between the two classes of spiral drawings. Displaying patients self ratings along with the spirals in the web application is another possible reason for this, as the neurologist may have relied too much on this in his own ratings.
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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. Some methods are exhibiting good results in certain conditions and others are good with different kinds of images. Image discriminating techniques are widely used for pattern and image analysis. Common discriminating methods are discussed.
SIPL, Mechatronics, GIST 1 Oryong-Dong, Buk-Gu, Gwangju, 500-712 South Korea tel. 0082-62-970-2997
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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 contrary, operating as a Stated-Owned Commercial Bank affects both EPS and ROA negatively. The empirical results also indicate that undergoing initial public offering on the Hong Kong Stock Exchange helps a bank to improve performance, while the listing in Mainland China does not.
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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; Mueller, Ferris et al. 2009). Simulation tools have become a critical part of the automotive industry due to their ability to reduce the time and money spent in the development process. Terrain is the principle source of vertical excitation to the vehicle and must be accurately represented in order to correctly predict the vehicle response in simulation. In this dissertation, non-deformable terrain surfaces are defined as a sequence of vectors, where each vector comprises terrain heights at locations oriented perpendicular to the direction of travel. The evolution and implications of terrain surface measurement techniques and existing methods for correcting INS drift are reviewed as a framework for a new compensation method for INS drift in terrain surface measurements. Each measurement is considered a combination of the true surface and the error surface, defined on a Hilbert vector space, in which the error is decomposed into drift (global error) and noise (local error). It is also desirable to develop a compact, path-specific, terrain surface representation that exploits the inherent anisotropicity in terrain over which vehicles traverse. In order to obtain this, a set of analytic basis vectors is formed from Gegenbauer polynomials, parameterized to approximate the empirical basis vectors of the true terrain surface. It is also desirable to evaluate vehicle models and tire models over a wide range of terrain types, but it is computationally impractical to store long distances of every terrain surface variation. This dissertation examines the terrain surface, rather than the terrain profile, to maximize the information available to the tire model (i.e. wheel path data). A method to decompose the terrain surface as a combination of deterministic and stochastic components is also developed.
Ph. D.
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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 Beschränkungen der jeweiligen Ansätze aufgezeigt. Im Bereich der Signalverarbeitung haben sich das Langsamkeitsprinzip und dessen direkte Implementierung als Signalverarbeitungsalgorithmus, Slow Feature Analysis, über die biologisch inspirierte Modellierung hinaus als nützlich erwiesen. Es wird ein neuer Algorithmus für das Problem der nichtlinearen blinden Signalquellentrennung beschrieben, der auf einer Kombination von Langsamkeitsprinzip und dem Prinzip der statistischen Unabhängigkeit basiert, und der anhand von künstlichen und realistischen Audiosignalen getestet wird. Außerdem wird die Open Source Software Bibliothek Modular toolkit for Data Processing vorgestellt.
In 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.
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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. New York: Springer, 2010.

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Hyvarinen, Aapo. Independent component analysis. New York: J. Wiley, 2001.

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

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A user's guide to principal components. Hoboken, N.J: Wiley-Interscience, 2003.

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

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

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

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Principal component analysis. New York: Springer-Verlag, 1986.

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Principal component analysis. 2nd ed. New York: Springer, 2002.

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

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Book chapters on the topic "Principle component analysis"

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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.

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Deutsch, 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.

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Kinser, 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.

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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, 111–19. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99695-0_14.

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Itoh, 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.

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Gatignon, 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.

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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, 958–65. Berlin, Heidelberg: 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, 362–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30499-9_55.

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Jamshidi, 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.

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Chen, 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.

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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), November 2003. http://dx.doi.org/10.2172/823317.

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MARTIN, SHAWN B. Kernel Near Principal Component Analysis. Office of Scientific and Technical Information (OSTI), July 2002. http://dx.doi.org/10.2172/810934.

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Aït-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.

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Eick, 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.

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Federer, 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.

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Federer, 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.

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Krishnaiah, 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.

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Thompson, 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.

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Fujikoshi, 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.

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Harter, 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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Abstract:
The 2015 Residential Energy Consumption Survey design called for stratification of primary sampling units to improve estimation. Two methods of defining strata from multiple stratification variables were proposed, leading to this investigation. All stratification methods use stratification variables available for the entire frame. We reviewed textbook guidance on the general principles and desirable properties of stratification variables and the assumptions on which the two methods were based. Using principal components combined with cluster analysis on the stratification variables to define strata focuses on relationships among stratification variables. Decision trees, regressions, and correlation approaches focus more on relationships between the stratification variables and prior outcome data, which may be available for just a sample of units. Using both principal components/cluster analysis and decision trees, we stratified primary sampling units for the 2009 Residential Energy Consumption Survey and compared the resulting strata.
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