Gotowe bibliografie tematyczne / Principal components analysis

Gotowa bibliografia na temat „Principal components analysis”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 30 lipca 2024

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Artykuły w czasopismach na temat "Principal components analysis"

Whitlark, David, i George H. Dunteman. "Principal Components Analysis". Journal of Marketing Research 27, nr 2 (maj 1990): 243. http://dx.doi.org/10.2307/3172855.

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Kim, Sung-Hoon, i George H. Dunteman. "Principal Components Analysis". Journal of Educational Statistics 16, nr 2 (1991): 141. http://dx.doi.org/10.2307/1165117.

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Fujiwara, Masakazu, Tomohiro Minamidani, Isamu Nagai i Hirofumi Wakaki. "Principal Components Regression by Using Generalized Principal Components Analysis". JOURNAL OF THE JAPAN STATISTICAL SOCIETY 43, nr 1 (2013): 57–78. http://dx.doi.org/10.14490/jjss.43.57.

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Abegaz, Fentaw, Kridsadakorn Chaichoompu, Emmanuelle Génin, David W. Fardo, Inke R. König, Jestinah M. Mahachie John i Kristel Van Steen. "Principals about principal components in statistical genetics". Briefings in Bioinformatics 20, nr 6 (14.09.2018): 2200–2216. http://dx.doi.org/10.1093/bib/bby081.

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Abstract Principal components (PCs) are widely used in statistics and refer to a relatively small number of uncorrelated variables derived from an initial pool of variables, while explaining as much of the total variance as possible. Also in statistical genetics, principal component analysis (PCA) is a popular technique. To achieve optimal results, a thorough understanding about the different implementations of PCA is required and their impact on study results, compared to alternative approaches. In this review, we focus on the possibilities, limitations and role of PCs in ancestry prediction, genome-wide association studies, rare variants analyses, imputation strategies, meta-analysis and epistasis detection. We also describe several variations of classic PCA that deserve increased attention in statistical genetics applications.

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Voegtlin, Thomas. "Recursive principal components analysis". Neural Networks 18, nr 8 (październik 2005): 1051–63. http://dx.doi.org/10.1016/j.neunet.2005.07.005.

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Maćkiewicz, Andrzej, i Waldemar Ratajczak. "Principal components analysis (PCA)". Computers & Geosciences 19, nr 3 (marzec 1993): 303–42. http://dx.doi.org/10.1016/0098-3004(93)90090-r.

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Yendle, Peter W., i Halliday J. H. MacFie. "Discriminant principal components analysis". Journal of Chemometrics 3, nr 4 (wrzesień 1989): 589–600. http://dx.doi.org/10.1002/cem.1180030407.

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Saegusa, Ryo, Hitoshi Sakano i Shuji Hashimoto. "Nonlinear principal component analysis to preserve the order of principal components". Neurocomputing 61 (październik 2004): 57–70. http://dx.doi.org/10.1016/j.neucom.2004.03.004.

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Rutledge, Douglas N. "Comparison of Principal Components Analysis, Independent Components Analysis and Common Components Analysis". Journal of Analysis and Testing 2, nr 3 (lipiec 2018): 235–48. http://dx.doi.org/10.1007/s41664-018-0065-5.

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Boudou, Alain, i Sylvie Viguier-Pla. "Principal components analysis and cyclostationarity". Journal of Multivariate Analysis 189 (maj 2022): 104875. http://dx.doi.org/10.1016/j.jmva.2021.104875.

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Rozprawy doktorskie na temat "Principal components analysis"

Nunes, Madalena Baioa Paraíso. "Portfolio selection : a study using principal component analysis". Master's thesis, Instituto Superior de Economia e Gestão, 2017. http://hdl.handle.net/10400.5/14598.

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Mestrado em Finanças
Nesta tese aplicámos a análise de componentes principais ao mercado bolsista português usando os constituintes do índice PSI-20, de Julho de 2008 a Dezembro de 2016. Os sete primeiros componentes principais foram retidos, por se ter verificado que estes representavam as maiores fontes de risco deste mercado em específico. Assim, foram construídos sete portfólios principais e comparámo-los com outras estratégias de alocação. Foram construídos o portfólio 1/N (portfólio com investimento igual para cada um dos 26 ativos), o PPEqual (portfólio com igual investimento em cada um dos 7 principal portfólios) e o portfólio MV (portfólio que tem por base a teoria moderna de gestão de carteiras de Markowitz (1952)). Concluímos que estes dois últimos portfólios apresentavam os melhores resultados em termos de risco e retorno, sendo o portfólio PPEqual mais adequado a um investidor com maior grau de aversão ao risco e o portfólio MV mais adequado a um investidor que estaria disposto a arriscar mais em prol de maior retorno. No que diz respeito ao nível de risco, o PPEqual é o portfólio com melhores resultados e nenhum outro portfólio conseguiu apresentar valores semelhantes. Assim encontrámos um portfólio que é a ponderação de todos os portfólios principais por nós construídos e este era o portfólio mais eficiente em termos de risco.
In this thesis we apply principal component analysis to the Portuguese stock market using the constituents of the PSI-20 index from July 2008 to December 2016. The first seven principal components were retained, as we verified that these represented the major risk sources in this specific market. Seven principal portfolios were constructed and we compared them with other allocation strategies. The 1/N portfolio (with an equal investment in each of the 26 stocks), the PPEqual portfolio (with an equal investment in each of the 7 principal portfolios) and the MV portfolio (based on Markowitz's (1952) mean-variance strategy) were constructed. We concluded that these last two portfolios presented the best results in terms of return and risk, with PPEqual portfolio being more suitable for an investor with a greater degree of risk aversion and the MV portfolio more suitable for an investor willing to risk more in favour of higher returns. Regarding the level of risk, PPEqual is the portfolio with the best results and, so far, no other portfolio has presented similar values. Therefore, we found an equally-weighted portfolio among all the principal portfolios we built, which was the most risk efficient.
info:eu-repo/semantics/publishedVersion

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Brubaker, S. Charles. "Extensions of principal components analysis". Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29645.

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Thesis (Ph.D)--Computing, Georgia Institute of Technology, 2009.
Committee Chair: Santosh Vempala; Committee Member: Adam Kalai; Committee Member: Haesun Park; Committee Member: Ravi Kannan; Committee Member: Vladimir Koltchinskii. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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Brandenberg, Romano Rodolfo. "Principal Components Analysis of Commodity Trading Advisors". St. Gallen, 2008. http://www.biblio.unisg.ch/org/biblio/edoc.nsf/wwwDisplayIdentifier/02604577002/$FILE/02604577002.pdf.

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Fučík, Vojtěch. "Principal component analysis in Finance". Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-264205.

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The main objective of this thesis is to summarize and possibly interconnect the existing methodology on principal components analysis, hierarchical clustering and topological organization in the financial and economic networks, linear regression and GARCH modeling. In the thesis the clustering ability of PCA is compared with the more conventional approaches on a set of world stock market indices returns in different time periods where the time division is represented by The World Financial Crisis of 2007-2009. It is also observed whether the clustering of DJIA index components is underlied by the industry sector to which the individual stocks belong. Joining together PCA with classical linear regression creates principal components regression which is further in the thesis applied to the German DAX 30 index logarithmic returns forecasting using various macroeconomic and financial predictors. The correlation between two energy stocks returns - Chevron and ExxonMobil is forecasted using orthogonal (or PCA) GARCH. The constructed forecast is then compared with the predictions constructed by the conventional multivariate volatility models - EWMA and DCC GARCH.

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Khwambala, Patricia Helen. "The importance of selecting the optimal number of principal components for fault detection using principal component analysis". Master's thesis, University of Cape Town, 2012. http://hdl.handle.net/11427/11930.

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Includes summary.
Includes bibliographical references.
Fault detection and isolation are the two fundamental building blocks of process monitoring. Accurate and efficient process monitoring increases plant availability and utilization. Principal component analysis is one of the statistical techniques that are used for fault detection. Determination of the number of PCs to be retained plays a big role in detecting a fault using the PCA technique. In this dissertation focus has been drawn on the methods of determining the number of PCs to be retained for accurate and effective fault detection in a laboratory thermal system. SNR method of determining number of PCs, which is a relatively recent method, has been compared to two commonly used methods for the same, the CPV and the scree test methods.

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Brennan, Victor L. "Principal component analysis with multiresolution". [Gainesville, Fla.] : University of Florida, 2001. http://etd.fcla.edu/etd/uf/2001/ank7079/brennan%5Fdissertation.pdf.

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Thesis (Ph. D.)--University of Florida, 2001.
Title from first page of PDF file. Document formatted into pages; contains xi, 124 p.; also contains graphics. Vita. Includes bibliographical references (p. 120-123).

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López, Alfageme Alfredo Ignacio. "Nonlinear principal components analysis for measures and images". Tesis, Universidad de Chile, 2013. http://www.repositorio.uchile.cl/handle/2250/114861.

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Doctor en Ciencias de la Ingeniería, Mención Modelación Matemática
En esta tesis definimos dos adaptaciones no-lineales del análisis de componentes principales, para el estudio de la variabilidad de datos conformados por medidas de probabilidad y por imágenes. En el Capitulo 2 introducimos el método de análisis de componentes principales geodésico (ACPG) en el espacio de medidas de probabilidad en la línea real, con segundo momento finito, dotado de la métrica de Wasserstein. Apoyándonos en la estructura pseudo-riemanniana del espacio de Wasserstein, definimos el ACPG basado en adaptaciones del ACP a variedades, propuestas en la literatura. En este contexto, el ACPG se define por medio de un problema de minimización sobre el espacio conformado por los subconjuntos geodésicos del espacio de Wasserstein. Usando argumentos de compacidad y de gama-convergencia, establecemos la consistencia del método, demostrando que el ACPG converge a su contraparte poblacional, cuando el tamaño de la muestra crece a infinito. Discutimos las ventajas de este método, respecto a un ACP funcional estándar de medidas de probabilidad en el espacio de Hilbert de funciones a cuadrado integrable. Con el fin de mostrar los beneficios de este procedimiento para el análisis de datos, exhibimos algunos ejemplos ilustrativos en un modelo estadístico simple. En el Capitulo 3 describimos el método de análisis de componentes principales geométrico (ACP geométrico) para analizar los modos principales de variación geométrica de un conjunto de imágenes. En este contexto proponemos modelar la variabilidad geométrica de las imágenes, respecto a un patrón medio de referencia, por medio de un operador de deformación parametrizado por un espacio de Hilbert. El ACP geométrico consta de dos etapas: (1) registro de imágenes usando un operador de deformación y (2) ACP estándar en los parámetros asociados a las deformaciones. La consistencia del procedimiento es analizada en el contexto de un modelo estadístico de patrón deformable, con una doble asíntota, donde el número de observaciones tiende a infinito y el ruido aditivo converge a cero. Para destacar los beneficios de este procedimiento, describimos un algoritmo y su aplicación a algunos experimentos numéricos con imágenes reales.

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Bhamani, Feroz. "Hedging Interest-Rate Options Using Principal Components Analysis". Master's thesis, University of Cape Town, 2018. http://hdl.handle.net/11427/29250.

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It is often a goal of the risk management of a portfolio of interest rate sensitive instruments to minimize the impact of movements in market rates on the value of the portfolio. This can be done by considering the sensitivity of the portfolio to each of the market rates that are used to bootstrap a yield curve. However, this is likely to lead to an excessive amount of trading due to an investment in a large number of hedging securities. As an alternative, we consider using principal components analysis (PCA) to condense most of the variability in the market rates into a much smaller number of risk factors, called the principal components. One can then construct a hedging portfolio so as to make the portfolio immune to shocks in these principal components, and hence to the most common movements in the yield curve. We compare the effectiveness of these two hedging strategies for hedging a portfolio of interest-rate options, both in the absence and presence of transaction costs. We also consider the additional feature of being able to update each hedging methodology on a daily basis and rebalance the hedge portfolios accordingly.

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Patak, Zdenek. "Robust principal component analysis via projection pursuit". Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/29737.

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In principal component analysis (PCA), the principal components (PC) are linear combinations of the variables that minimize some objective function. In the classical setup the objective function is the variance of the PC's. The variance of the PC's can be easily upset by outlying observations; hence, Chen and Li (1985) proposed a robust alternative for the PC's obtained by replacing the variance with an M-estimate of scale. This approach cannot achieve a high breakdown point (BP) and efficiency at the same time. To obtain both high BP and efficiency, we propose to use MM- and τ-estimates in place of the M-estimate. Although outliers may cause bias in both the direction and the size of the PC's, Chen and Li looked at the scale bias only, whereas we consider both. All proposed robust methods are based on the minimization of a non-convex objective function; hence, a good initial starting point is required. With this in mind, we propose an orthogonal version of the least median of squares (Rousseeuw and Leroy, 1987) and a new method that is orthogonal equivariant, robust and easy to compute. Extensive Monte Carlo study shows promising results for the proposed method. Orthogonal regression and detection of multivariate outliers are discussed as possible applications of PCA.
Science, Faculty of
Statistics, Department of
Graduate

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Wedlake, Ryan Stuart. "Robust principal component analysis biplots". Thesis, Link to the online version, 2008. http://hdl.handle.net/10019/929.

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Książki na temat "Principal components analysis"

Dunteman, George H. Principal components analysis. Newbury Park: Sage Publications, 1989.

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Dunteman, George H. Principal components analysis. Newbury Park: Sage Publications, 1989.

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Dunteman, George. Principal Components Analysis. 2455 Teller Road, Newbury Park California 91320 United States of America: SAGE Publications, Inc., 1989. http://dx.doi.org/10.4135/9781412985475.

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Jolliffe, I. T. Principal component analysis. Wyd. 2. New York: Springer, 2010.

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

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Juha, Karhunen, i Oja Erkki, red. Independent component analysis. New York: J. Wiley, 2001.

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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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LeBlanc, Michael R. Adaptive principal surfaces. Toronto: University of Toronto, Dept. of Statistics, 1991.

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J, Dunn W., Scott D. R. 1934- i United States. Environmental Protection Agency., red. Principal components analysis and partial least squares regression. [Washington, D.C.?: U.S. Environmental Protection Agency, 1992.

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Części książek na temat "Principal components analysis"

Murtagh, Fionn, i André Heck. "Principal Components Analysis". W Multivariate Data Analysis, 13–53. Dordrecht: Springer Netherlands, 1987. http://dx.doi.org/10.1007/978-94-009-3789-5_2.

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Hilbert, Sven, i Markus Bühner. "Principal Components Analysis". W Encyclopedia of Personality and Individual Differences, 4030–34. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-24612-3_1340.

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Härdle, Wolfgang, i Léopold Simar. "Principal Components Analysis". W Applied Multivariate Statistical Analysis, 233–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05802-2_9.

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Everitt, Brian S., i Graham Dunn. "Principal Components Analysis". W Applied Multivariate Data Analysis, 48–73. West Sussex, United Kingdom: John Wiley & Sons, Ltd,., 2013. http://dx.doi.org/10.1002/9781118887486.ch3.

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Härdle, Wolfgang Karl, i Léopold Simar. "Principal Components Analysis". W Applied Multivariate Statistical Analysis, 319–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-45171-7_11.

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Everitt, Brian Sidney. "Principal Components Analysis". W Springer Texts in Statistics, 41–64. London: Springer London, 2005. http://dx.doi.org/10.1007/1-84628-124-5_3.

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Härdle, Wolfgang Karl, i Léopold Simar. "Principal Components Analysis". W Applied Multivariate Statistical Analysis, 269–305. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-17229-8_10.

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Atkinson, Anthony C., Marco Riani i Andrea Cerioli. "Principal Components Analysis". W Springer Series in Statistics, 229–96. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-0-387-21840-3_5.

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Afifi, A. A., i V. Clark. "Principal components analysis". W Computer-Aided Multivariate Analysis, 330–53. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4899-3342-3_14.

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Frieden, B. Roy. "Principal Components Analysis". W Probability, Statistical Optics, and Data Testing, 363–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-56699-8_15.

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Streszczenia konferencji na temat "Principal components analysis"

Guo, Hao, Kurt J. Marfurt, Jianlei Liu i Qifeng Dou. "Principal components analysis of spectral components". W SEG Technical Program Expanded Abstracts 2006. Society of Exploration Geophysicists, 2006. http://dx.doi.org/10.1190/1.2370422.

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Boutsidis, Christos, Dan Garber, Zohar Karnin i Edo Liberty. "Online Principal Components Analysis". W Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2014. http://dx.doi.org/10.1137/1.9781611973730.61.

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Wei-min, Liu, i Chang Chein-I. "Variants of Principal Components Analysis". W 2007 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2007. http://dx.doi.org/10.1109/igarss.2007.4422989.

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Firmansyah, Gerry, Zainal A. Hasibuan i Yudho Giri Sucahyo. "Indonesia e-Government components: A principal component analysis approach". W 2014 International Conference on Information Technology Systems and Innovation (ICITSI). IEEE, 2014. http://dx.doi.org/10.1109/icitsi.2014.7048255.

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Steinherz, T., N. Intrator i E. Rivlin. "Skew detection via principal components analysis". W Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318). IEEE, 1999. http://dx.doi.org/10.1109/icdar.1999.791747.

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Ghisu, Tiziano, Geoffrey Parks, Jerome Jarrett i P. Clarkson. "Accelerating Design Optimization Via Principal Components Analysis". W 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2008. http://dx.doi.org/10.2514/6.2008-5855.

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Allen, Genevera I. "Multi-way functional principal components analysis". W 2013 IEEE 5th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, 2013. http://dx.doi.org/10.1109/camsap.2013.6714047.

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Couture, V. Chapdelaine, S. Roy, M. S. Langer i R. Mann. "Principal Components Analysis of Optical Snow". W British Machine Vision Conference 2004. British Machine Vision Association, 2004. http://dx.doi.org/10.5244/c.18.82.

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Pakatci, Isa Kemal, Wei Wang i Leonard McMillan. "Gene set analysis using principal components". W the First ACM International Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1854776.1854822.

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Allen, Genevera I., i Michael Weylandt. "Sparse and Functional Principal Components Analysis". W 2019 IEEE Data Science Workshop (DSW). IEEE, 2019. http://dx.doi.org/10.1109/dsw.2019.8755778.

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Raporty organizacyjne na temat "Principal components analysis"

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

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Ayres, João, Arturo Galindo, Santiago Novoa i Victoria Nuguer. Inflation Dynamics in Latin America and the Caribbean. Inter-American Development Bank, marzec 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 are major drivers of the surge in inflation in the region post-COVID-19 lockdowns.

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Harter, Rachel M., Pinliang (Patrick) Chen, Joseph P. McMichael, Edgardo S. Cureg, Samson A. Adeshiyan i Katherine B. Morton. Constructing Strata of Primary Sampling Units for the Residential Energy Consumption Survey. RTI Press, maj 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0041.1705.

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

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

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Aït-Sahalia, Yacine, i Dacheng Xiu. Principal Component Analysis of High Frequency Data. Cambridge, MA: National Bureau of Economic Research, wrzesień 2015. http://dx.doi.org/10.3386/w21584.

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Velez, Gladis, i Ragvi Shah. Reorienting Smart City Metrics to Emphasize Resident Well-Being: A Disparity-Oriented Approach. University of Miami, 2022. http://dx.doi.org/10.33596/report-1.

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This paper applies a disparity-oriented focus to promote human-centered solutions to smart city planning efforts. For five metropolitan areas (San Jose, Miami, New York, Denver, and Seattle) we explored three smart city domains (socioeconomics, public transit access, and digital divide), identified candidate indicators for each domain using publicly available data, and mapped composite measures generated using principal components analysis. The study identifies areas that may be most and least likely to benefit from smart city investments. Reorienting solutions can ultimately increase community equity and engagement in urban life.

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Eick, Brian, Zachary Treece, Billie Spencer, Matthew Smith, Steven Sweeney, Quincy Alexander i Stuart Foltz. Miter gate gap detection using principal component analysis. Engineer Research and Development Center (U.S.), czerwiec 2018. http://dx.doi.org/10.21079/11681/27365.

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Zhao, George, Grang Mei, Bulent Ayhan, Chiman Kwan i Venu Varma. DTRS57-04-C-10053 Wave Electromagnetic Acoustic Transducer for ILI of Pipelines. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), marzec 2005. http://dx.doi.org/10.55274/r0012049.

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In this project, Intelligent Automation, Incorporated (IAI) and Oak Ridge National Lab (ORNL) propose a novel and integrated approach to inspect the mechanical dents and metal loss in pipelines. It combines the state-of-the-art SH wave Electromagnetic Acoustic Transducer (EMAT) technique, through detailed numerical modeling, data collection instrumentation, and advanced signal processing and pattern classifications, to detect and characterize mechanical defects in the underground pipeline transportation infrastructures. The technique has four components: (1) thorough guided wave modal analysis, (2) recently developed three-dimensional (3-D) Boundary Element Method (BEM) for best operational condition selection and defect feature extraction, (3) ultrasonic Shear Horizontal (SH) waves EMAT sensor design and data collection, and (4) advanced signal processing algorithm like a nonlinear split-spectrum filter, Principal Component Analysis (PCA) and Discriminant Analysis (DA) for signal-to-noise-ratio enhancement, crack signature extraction, and pattern classification. This technology not only can effectively address the problems with the existing methods, i.e., to detect the mechanical dents and metal loss in the pipelines consistently and reliably but also it is able to determine the defect shape and size to a certain extent.

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

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