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.
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 textPham, Duong Hung. "Contributions to the analysis of multicomponent signals : synchrosqueezing and associated methods." Thesis, Université Grenoble Alpes (ComUE), 2018. http://www.theses.fr/2018GREAM044/document.
Full textMany physical signals including audio (music, speech), medical data (ECG, PCG), marine mammals or gravitational-waves can be accurately modeled as a superposition of amplitude and frequency-modulated waves (AM-FM modes), called multicomponent signals (MCSs). Time-frequency (TF) analysis plays a central role in characterizing such signals and in that framework, numerous methods have been proposed over the last decade. However, these methods suffer from an intrinsic limitation known as the uncertainty principle. In this regard, reassignment method (RM) was developed with the purpose of sharpening TF representations (TFRs) given respectively by the short-time Fourier transform (STFT) or the continuous wavelet transform (CWT). Unfortunately, it did not allow for mode reconstruction, in opposition to its recent variant known as synchrosqueezing transforms (SST). Nevertheless, many critical problems associated with the latter still remain to be addressed such as the weak frequency modulation condition, the mode retrieval of an MCS from its downsampled STFT or the TF signature estimation of irregular and discontinuous signals. This dissertation mainly deals with such problems in order to provide more powerful and accurate invertible TF methods for analyzing MCSs.This dissertation gives six valuable contributions. The first one introduces a second-order extension of wavelet-based SST along with a discussion on its theoretical analysis and practical implementation. The second one puts forward a generalization of existing STFT-based synchrosqueezing techniques known as the high-order STFT-based SST (FSSTn) that enables to better handle a wide range of MCSs. The third one proposes a new technique established on the second-order STFT-based SST (FSST2) and demodulation procedure, called demodulation-FSST2-based technique (DSST2), enabling a better performance of mode reconstruction. The fourth contribution is that of a novel approach allowing for the retrieval of modes of an MCS from its downsampled STFT. The fifth one presents an improved method developed in the reassignment framework, called adaptive contour representation computation (ACRC), for an efficient estimation of TF signatures of a larger class of MCSs. The last contribution is that of a joint analysis of ACRC with non-negative matrix factorization (NMF) to enable an effective denoising of phonocardiogram (PCG) signals
Andersson, Moa. "Who supports non-traditional gender roles? : Exploring the Relationship Between Self-interest, Contextual Exposure and Gender Attitudes in Sweden." Thesis, Stockholms universitet, Sociologiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-118772.
Full textAbuasbeh, Mohammad. "Fault Detection and Diagnosis for Brine to Water Heat Pump Systems." Thesis, KTH, Tillämpad termodynamik och kylteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-183595.
Full textUygun, Nazli. "Validity Of Science Items In The Student Selection Test In Turkey." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/3/12609716/index.pdf.
Full textVadapalli, Hima Bindu. "Recognition of facial action units from video streams with recurrent neural networks : a new paradigm for facial expression recognition." University of the Western Cape, 2011. http://hdl.handle.net/11394/5415.
Full textThis research investigated the application of recurrent neural networks (RNNs) for recognition of facial expressions based on facial action coding system (FACS). Support vector machines (SVMs) were used to validate the results obtained by RNNs. In this approach, instead of recognizing whole facial expressions, the focus was on the recognition of action units (AUs) that are defined in FACS. Recurrent neural networks are capable of gaining knowledge from temporal data while SVMs, which are time invariant, are known to be very good classifiers. Thus, the research consists of four important components: comparison of the use of image sequences against single static images, benchmarking feature selection and network optimization approaches, study of inter-AU correlations by implementing multiple output RNNs, and study of difference images as an approach for performance improvement. In the comparative studies, image sequences were classified using a combination of Gabor filters and RNNs, while single static images were classified using Gabor filters and SVMs. Sets of 11 FACS AUs were classified by both approaches, where a single RNN/SVM classifier was used for classifying each AU. Results indicated that classifying FACS AUs using image sequences yielded better results than using static images. The average recognition rate (RR) and false alarm rate (FAR) using image sequences was 82.75% and 7.61%, respectively, while the classification using single static images yielded a RR and FAR of 79.47% and 9.22%, respectively. The better performance by the use of image sequences can be at- tributed to RNNs ability, as stated above, to extract knowledge from time-series data. Subsequent research then investigated benchmarking dimensionality reduction, feature selection and network optimization techniques, in order to improve the performance provided by the use of image sequences. Results showed that an optimized network, using weight decay, gave best RR and FAR of 85.38% and 6.24%, respectively. The next study was of the inter-AU correlations existing in the Cohn-Kanade database and their effect on classification models. To accomplish this, a model was developed for the classification of a set of AUs by a single multiple output RNN. Results indicated that high inter-AU correlations do in fact aid classification models to gain more knowledge and, thus, perform better. However, this was limited to AUs that start and reach apex at almost the same time. This suggests the need for availability of a larger database of AUs, which could provide both individual and AU combinations for further investigation. The final part of this research investigated use of difference images to track the motion of image pixels. Difference images provide both noise and feature reduction, an aspect that was studied. Results showed that the use of difference image sequences provided the best results, with RR and FAR of 87.95% and 3.45%, respectively, which is shown to be significant when compared to use of normal image sequences classified using RNNs. In conclusion, the research demonstrates that use of RNNs for classification of image sequences is a new and improved paradigm for facial expression recognition.
Grener, Doreen Elaine. "A Content Analysis of Elementary Science Textbook Series From 1930 Through 1990 for The Presentation of The Principle of Humans as a Component of The Ecosystem /." The Ohio State University, 1996. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487933648650631.
Full textBršlíková, Jana. "Analýza úmrtnostních tabulek pomocí vybraných vícerozměrných statistických metod." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-201859.
Full textNunes, 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.
Full textNesta 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.
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Koyuncu, Fulya. "Validity Of Biology Items In 2006, 2007, And 2008 Student Selection Test In Turkey." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12612946/index.pdf.
Full texthigher order thinking skills like analytical thinking, interpretation and reasoning about elementary school curriculum and 9th grade curricula objectives. On the other hand, second part of the test aims to assess students&rsquo
higher order thinking skills given in the high school curriculum. The main aim of this thesis is to analyze to what extend 2006, 2007 and 2008 student selection tests biology items assess higher order cognitive skills. In accordance with this purpose, elementary and high school curriculum and the appropriateness of the questions in the student selection test with the educational objectives of the curriculum are examined. In addition, dimensions of 2006, 2007, and 2008 SST biology items are examined by Exploratory Component Analysis and Confirmatory Component Analysis techniques. The result of those analysis revealed that SST biology items mostly focus on remembering skill and fail to assess higher order thinking skills. Additionally, there is not any consistency among 2006, 2007, and 2008 SSTs biology items in terms of dimensions which means there is not any construct in biology subtests of SSTs. The other aim of the present study is to identify how much academic and non-academic factors explain the biology achievement. While for academic factors reading comprehension, mathematics, physics, and chemistry achievements of students are used, age, gender, and school type are used for non-academic factors. Findings of the research revealed that academic factors, especially chemistry achievement, have significant affect on biology achievement. In terms of non-academic factors, graduating from selecting high school has important role for biology achievement. Additionally, older students and girls tend to have higher grades in biology.
Brand, Hilmarie. "PCA and CVA biplots : a study of their underlying theory and quality measures." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/80363.
Full textENGLISH ABSTRACT: The main topics of study in this thesis are the Principal Component Analysis (PCA) and Canonical Variate Analysis (CVA) biplots, with the primary focus falling on the quality measures associated with these biplots. A detailed study of different routes along which PCA and CVA can be derived precedes the study of the PCA biplot and CVA biplot respectively. Different perspectives on PCA and CVA highlight different aspects of the theory that underlie PCA and CVA biplots respectively and so contribute to a more solid understanding of these biplots and their interpretation. PCA is studied via the routes followed by Pearson (1901) and Hotelling (1933). CVA is studied from the perspectives of Linear Discriminant Analysis, Canonical Correlation Analysis as well as a two-step approach introduced in Gower et al. (2011). The close relationship between CVA and Multivariate Analysis of Variance (MANOVA) also receives some attention. An explanation of the construction of the PCA biplot is provided subsequent to the study of PCA. Thereafter follows an in depth investigation of quality measures of the PCA biplot as well as the relationships between these quality measures. Specific attention is given to the effect of standardisation on the PCA biplot and its quality measures. Following the study of CVA is an explanation of the construction of the weighted CVA biplot as well as two different unweighted CVA biplots based on the two-step approach to CVA. Specific attention is given to the effect of accounting for group sizes in the construction of the CVA biplot on the representation of the group structure underlying a data set. It was found that larger groups tend to be better separated from other groups in the weighted CVA biplot than in the corresponding unweighted CVA biplots. Similarly it was found that smaller groups tend to be separated to a greater extent from other groups in the unweighted CVA biplots than in the corresponding weighted CVA biplot. A detailed investigation of previously defined quality measures of the CVA biplot follows the study of the CVA biplot. It was found that the accuracy with which the group centroids of larger groups are approximated in the weighted CVA biplot is usually higher than that in the corresponding unweighted CVA biplots. Three new quality measures that assess that accuracy of the Pythagorean distances in the CVA biplot are also defined. These quality measures assess the accuracy of the Pythagorean distances between the group centroids, the Pythagorean distances between the individual samples and the Pythagorean distances between the individual samples and group centroids in the CVA biplot respectively.
AFRIKAANSE OPSOMMING: Die hoofonderwerpe van studie in hierdie tesis is die Hoofkomponent Analise (HKA) bistipping asook die Kanoniese Veranderlike Analise (KVA) bistipping met die primêre fokus op die kwaliteitsmaatstawwe wat daarmee geassosieer word. ’n Gedetailleerde studie van verskillende roetes waarlangs HKA en KVA afgelei kan word, gaan die studie van die HKA en KVA bistippings respektiewelik vooraf. Verskillende perspektiewe op HKA en KVA belig verskillende aspekte van die teorie wat onderliggend is tot die HKA en KVA bistippings respektiewelik en dra sodoende by tot ’n meer breedvoerige begrip van hierdie bistippings en hulle interpretasies. HKA word bestudeer volgens die roetes wat gevolg is deur Pearson (1901) en Hotelling (1933). KVA word bestudeer vanuit die perspektiewe van Linieêre Diskriminantanalise, Kanoniese Korrelasie-analise sowel as ’n twee-stap-benadering soos voorgestel in Gower et al. (2011). Die noue verwantskap tussen KVA en Meerveranderlike Analise van Variansie (MANOVA) kry ook aandag. ’n Verduideliking van die konstruksie van die HKA bistipping word voorsien na afloop van die studie van HKA. Daarna volg ’n indiepte-ondersoek van die HKA bistipping kwaliteitsmaatstawwe sowel as die onderlinge verhoudings tussen hierdie kwaliteitsmaatstawe. Spesifieke aandag word gegee aan die effek van die standaardisasie op die HKA bistipping en sy kwaliteitsmaatstawe. Opvolgend op die studie van KVA is ’n verduideliking van die konstruksie van die geweegde KVA bistipping sowel as twee veskillende ongeweegde KVA bistippings gebaseer op die twee-stap-benadering tot KVA. Spesifieke aandag word gegee aan die effek wat die inagneming van die groepsgroottes in die konstruksie van die KVA bistipping op die voorstelling van die groepstruktuur onderliggend aan ’n datastel het. Daar is gevind dat groter groepe beter geskei is van ander groepe in die geweegde KVA bistipping as in die oorstemmende ongeweegde KVA bistipping. Soortgelyk daaraan is gevind dat kleiner groepe tot ’n groter mate geskei is van ander groepe in die ongeweegde KVA bistipping as in die oorstemmende geweegde KVA bistipping. ’n Gedetailleerde ondersoek van voorheen gedefinieerde kwaliteitsmaatstawe van die KVA bistipping volg op die studie van die KVA bistipping. Daar is gevind dat die akkuraatheid waarmee die groepsgemiddeldes van groter groepe benader word in die geweegde KVA bistipping, gewoonlik hoër is as in die ooreenstemmende ongeweegde KVA bistippings. Drie nuwe kwaliteitsmaatstawe wat die akkuraatheid van die Pythagoras-afstande in die KVA bistipping meet, word gedefinieer. Hierdie kwaliteitsmaatstawe beskryf onderskeidelik die akkuraatheid van die voorstelling van die Pythagoras-afstande tussen die groepsgemiddeldes, die Pythagoras-afstande tussen die individuele observasies en die Pythagoras-afstande tussen die individuele observasies en groepsgemiddeldes in die KVA bistipping.
Nandong, Jobrun. "Modelling and control strategies for extractive alcoholic fermentation: partial control approach." Thesis, Curtin University, 2010. http://hdl.handle.net/20.500.11937/2197.
Full textZhang, Yuyao. "Non-linear dimensionality reduction and sparse representation models for facial analysis." Thesis, Lyon, INSA, 2014. http://www.theses.fr/2014ISAL0019/document.
Full textFace analysis techniques commonly require a proper representation of images by means of dimensionality reduction leading to embedded manifolds, which aims at capturing relevant characteristics of the signals. In this thesis, we first provide a comprehensive survey on the state of the art of embedded manifold models. Then, we introduce a novel non-linear embedding method, the Kernel Similarity Principal Component Analysis (KS-PCA), into Active Appearance Models, in order to model face appearances under variable illumination. The proposed algorithm successfully outperforms the traditional linear PCA transform to capture the salient features generated by different illuminations, and reconstruct the illuminated faces with high accuracy. We also consider the problem of automatically classifying human face poses from face views with varying illumination, as well as occlusion and noise. Based on the sparse representation methods, we propose two dictionary-learning frameworks for this pose classification problem. The first framework is the Adaptive Sparse Representation pose Classification (ASRC). It trains the dictionary via a linear model called Incremental Principal Component Analysis (Incremental PCA), tending to decrease the intra-class redundancy which may affect the classification performance, while keeping the extra-class redundancy which is critical for sparse representation. The other proposed work is the Dictionary-Learning Sparse Representation model (DLSR) that learns the dictionary with the aim of coinciding with the classification criterion. This training goal is achieved by the K-SVD algorithm. In a series of experiments, we show the performance of the two dictionary-learning methods which are respectively based on a linear transform and a sparse representation model. Besides, we propose a novel Dictionary Learning framework for Illumination Normalization (DL-IN). DL-IN based on sparse representation in terms of coupled dictionaries. The dictionary pairs are jointly optimized from normally illuminated and irregularly illuminated face image pairs. We further utilize a Gaussian Mixture Model (GMM) to enhance the framework's capability of modeling data under complex distribution. The GMM adapt each model to a part of the samples and then fuse them together. Experimental results demonstrate the effectiveness of the sparsity as a prior for patch-based illumination normalization for face images
Dickens, Peter Martin. "Facilitating Emergence: Complex, Adaptive Systems Theory and the Shape of Change." Antioch University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=antioch1339016565.
Full textGunnarsson, Fredrik. "Filtered Historical SimulationValue at Risk for Options : A Dimension Reduction Approach to Model the VolatilitySurface Shifts." Thesis, Umeå universitet, Institutionen för fysik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160344.
Full textKpamegan, Neil Racheed. "Robust Principal Component Analysis." Thesis, American University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10784806.
Full textIn multivariate analysis, principal component analysis is a widely popular method which is used in many different fields. Though it has been extensively shown to work well when data follows multivariate normality, classical PCA suffers when data is heavy-tailed. Using PCA with the assumption that the data follows a stable distribution, we will show through simulations that a new method is better. We show the modified PCA can be used for heavy-tailed data and that we can more accurately estimate the correct number of components compared to classical PCA and more accurately identify the subspace spanned by the important components.
Akinduko, Ayodeji Akinwumi. "Multiscale principal component analysis." Thesis, University of Leicester, 2016. http://hdl.handle.net/2381/36616.
Full textDer, Ralf, Ulrich Steinmetz, Gerd Balzuweit, and Gerrit Schüürmann. "Nonlinear principal component analysis." Universität Leipzig, 1998. https://ul.qucosa.de/id/qucosa%3A34520.
Full textSolat, Karo. "Generalized Principal Component Analysis." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/83469.
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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.
Full textIncludes 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.
Fučík, Vojtěch. "Principal component analysis in Finance." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-264205.
Full textWedlake, Ryan Stuart. "Robust principal component analysis biplots." Thesis, Link to the online version, 2008. http://hdl.handle.net/10019/929.
Full textBrennan, Victor L. "Principal component analysis with multiresolution." [Gainesville, Fla.] : University of Florida, 2001. http://etd.fcla.edu/etd/uf/2001/ank7079/brennan%5Fdissertation.pdf.
Full textTitle from first page of PDF file. Document formatted into pages; contains xi, 124 p.; also contains graphics. Vita. Includes bibliographical references (p. 120-123).
Phala, Adeela Colyne. "Application of multivariate regression techniques to paint: for the quantitive FTIR spectroscopic analysis of polymeric components." Thesis, Cape Peninsula University of Technology, 2011. http://hdl.handle.net/20.500.11838/733.
Full textIt is important to quantify polymeric components in a coating because they greatly influence the performance of a coating. The difficulty associated with analysis of polymers by Fourier transform infrared (FTIR) analysis’s is that colinearities arise from similar or overlapping spectral features. A quantitative FTIR method with attenuated total reflectance coupled to multivariate/ chemometric analysis is presented. It allows for simultaneous quantification of 3 polymeric components; a rheology modifier, organic opacifier and styrene acrylic binder, with no prior extraction or separation from the paint. The factor based methods partial least squares (PLS) and principle component regression (PCR) permit colinearities by decomposing the spectral data into smaller matrices with principle scores and loading vectors. For model building spectral information from calibrators and validation samples at different analysis regions were incorporated. PCR and PLS were used to inspect the variation within the sample set. The PLS algorithms were found to predict the polymeric components the best. The concentrations of the polymeric components in a coating were predicted with the calibration model. Three PLS models each with different analysis regions yielded a coefficient of correlation R2 close to 1 for each of the components. The root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) was less than 5%. The best out-put was obtained where spectral features of water was included (Trial 3). The prediction residual values for the three models ranged from 2 to -2 and 10 to -10. The method allows paint samples to be analysed in pure form and opens many opportunities for other coating components to be analysed in the same way.
Jahirul, Md Islam. "Experimental and statistical investigation of Australian native plants for second-generation biodiesel production." Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/83778/9/Jahirul_Islam_Thesis.pdf.
Full textCadima, Jorge Filipe Campinos Landerset. "Topics in descriptive Principal Component Analysis." Thesis, University of Kent, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.314686.
Full textLee, Colin K. "Infrared face recognition." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Jun%5FLee%5FColin.pdf.
Full textThesis advisor(s): Monique P. Fargues, Gamani Karunasiri. Includes bibliographical references (p. 135-136). Also available online.
Isaac, Benjamin. "Principal component analysis based combustion models." Doctoral thesis, Universite Libre de Bruxelles, 2014. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209278.
Full textDoctorat en Sciences de l'ingénieur
info:eu-repo/semantics/nonPublished
Alfonso, Miñambres Javier de. "Face recognition using principal component analysis." Master's thesis, Universidade de Aveiro, 2010. http://hdl.handle.net/10773/10221.
Full textThe purpose of this dissertation was to analyze the image processing method known as Principal Component Analysis (PCA) and its performance when applied to face recognition. This algorithm spans a subspace (called facespace) where the faces in a database are represented with a reduced number of features (called feature vectors). The study focused on performing various exhaustive tests to analyze in what conditions it is best to apply PCA. First, a facespace was spanned using the images of all the people in the database. We obtained then a new representation of each image by projecting them onto this facespace. We measured the distance between the projected test image with the other projections and determined that the closest test-train couple (k-Nearest Neighbour) was the recognized subject. This first way of applying PCA was tested with the Leave{One{Out test. This test takes an image in the database for test and the rest to build the facespace, and repeats the process until all the images have been used as test image once, adding up the successful recognitions as a result. The second test was to perform an 8{Fold Cross{Validation, which takes ten images as eligible test images (there are 10 persons in the database with eight images each) and uses the rest to build the facespace. All test images are tested for recognition in this fold, and the next fold is carried out, until all eight folds are complete, showing a different set of results. The other way to use PCA we used was to span what we call Single Person Facespaces (SPFs, a group of subspaces, each spanned with images of a single person) and measure subspace distance using the theory of principal angles. Since the database is small, a way to synthesize images from the existing ones was explored as a way to overcoming low successful recognition rates. All of these tests were performed for a series of thresholds (a variable which selected the number of feature vectors the facespaces were built with, i.e. the facespaces' dimension), and for the database after being preprocessed in two different ways in order to reduce statistically redundant information. The results obtained throughout the tests were within what expected from what can be read in literature: success rates of around 85% in some cases. Special mention needs to be made on the great result improvement between SPFs before and after extending the database with synthetic images. The results revealed that using PCA to project the images in the group facespace is very accurate for face recognition, even when having a small number of samples per subject. Comparing personal facespaces is more effective when we can synthesize images or have a natural way of acquiring new images of the subject, like for example using video footage. The tests and results were obtained with a custom software with user interface, designed and programmed by the author of this dissertation.
O propósito desta Dissertação foi a aplicação da Analise em Componentes Principais (PCA, de acordo com as siglas em inglês), em sistemas para reconhecimento de faces. Esta técnica permite calcular um subespaço (chamado facespace, onde as imagens de uma base de dados são representadas por um número reduzido de características (chamadas feature vectors). O estudo realizado centrou-se em vários testes para analisar quais são as condições óptimas para aplicar o PCA. Para começar, gerou-se um faces- pace utilizando todas as imagens da base de dados. Obtivemos uma nova representação de cada imagem, após a projecção neste espaço, e foram medidas as distâncias entre as projecções da imagem de teste e as de treino. A dupla de imagens de teste-treino mais próximas determina o sujeito reconhecido (classificador vizinhos mais próximos). Esta primeira forma de aplicar o PCA, e o respectivo classificador, foi avaliada com as estratégias Leave{One{Out e 8{Fold Cross{Validation. A outra forma de utilizar o PCA foi gerando subespaços individuais (designada por SPF, Single Person Facespace), onde cada subespaço era gerado com imagens de apenas uma pessoa, para a seguir medir a distância entre estes espaços utilizando o conceito de ângulos principais. Como a base de dados era pequena, foi explorada uma forma de sintetizar novas imagens a partir das já existentes. Todos estes teste foram feitos para uma série de limiares (uma variável threshold que determinam o número de feature vectors com os que o faces- pace é construído) e diferentes formas de pre-processamento. Os resultados obtidos estavam dentro do esperado: taxas de acerto aproximadamente iguais a 85% em alguns casos. Pode destacar-se uma grande melhoria na taxa de reconhecimento após a inclusão de imagens sintéticas na base de dados. Os resultados revelaram que o uso do PCA para projectar imagens no subespaço da base de dados _e viável em sistemas de reconhecimento de faces, principalmente se comparar subespaço individuais no caso de base de dados com poucos exemplares em que _e possível sintetizar imagens ou em sistemas com captura de vídeo.
Brubaker, S. Charles. "Extensions of principal components analysis." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/29645.
Full textCommittee 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.
Broadbent, Lane David. "Recognition of Infrastructure Events Using Principal Component Analysis." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/6197.
Full textTeixeira, Sérgio Coichev. "Utilização de análise de componentes principais em séries temporais." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-09052013-224741/.
Full textThe main objective of principal component analysis (PCA) is to reduce the number of variables in a small uncorrelated data sets, providing support and helping researcher understand the variation present in all the original variables with small uncorrelated amount of variables, called components. The principal components analysis is very simple and frequently used in several areas. For its construction, the components are calculated through covariance matrix. However, the covariance matrix does not capture the autocorrelation information, wasting important information about data sets. In this research, we present some techniques related to principal component analysis, considering autocorrelation information. However, we explore the principal component analysis in the domain frequency, providing more accurate and detailed results than classical component analysis time series case. In subsequent method SSA (Singular Spectrum Analysis) and MSSA (Multichannel Singular Spectrum Analysis), we study the principal component analysis considering relationship between locations and time points. These techniques are broadly used for atmospheric data sets to identify important characteristics and patterns, such as tendency and periodicity.
Burka, Zak. "Perceptual audio classification using principal component analysis /." Online version of thesis, 2010. http://hdl.handle.net/1850/12247.
Full textPatak, Zdenek. "Robust principal component analysis via projection pursuit." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/29737.
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Statistics, Department of
Graduate
Monahan, Adam Hugh. "Nonlinear principal component analysis of climate data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ48678.pdf.
Full textNilsson, Jakob, and Tim Lestander. "Detecting network failures using principal component analysis." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-132258.
Full textDauwe, Alexander. "Principal component analysis of the yield curve." Master's thesis, NSBE - UNL, 2009. http://hdl.handle.net/10362/9439.
Full textThis report deals with one of the remaining key problems in financial decision taking: the forecast of the term structure at different time horizons. Specifically: I will forecast the Euro Interest Rate Swap with a macro factor augmented autoregressive principal component model. I achieve forecasts that significantly outperform the Random Walk for medium to long term horizons when using a short rolling time window. Including macro factors leads to even better results.
Graner, Johannes. "On Asymptotic Properties of Principal Component Analysis." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-420649.
Full textLi, Liubo Li. "Trend-Filtered Projection for Principal Component Analysis." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1503277234178696.
Full textRoy, Samita. "Pyrite oxidation in coal-bearing strata : controls on in-situ oxidation as a precursor of acid mine drainage formation." Thesis, Durham University, 2002. http://etheses.dur.ac.uk/3753/.
Full textJaneček, David. "Sdružená EEG-fMRI analýza na základě heuristického modelu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221334.
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