Tesis sobre el tema "Principal component analysis"
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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.
Texto completoNesta 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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Kpamegan, Neil Racheed. "Robust Principal Component Analysis". Thesis, American University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10784806.
Texto completoIn 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.
Texto completoDer, Ralf, Ulrich Steinmetz, Gerd Balzuweit y Gerrit Schüürmann. "Nonlinear principal component analysis". Universität Leipzig, 1998. https://ul.qucosa.de/id/qucosa%3A34520.
Texto completoSolat, Karo. "Generalized Principal Component Analysis". Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/83469.
Texto completoPh. D.
Fučík, Vojtěch. "Principal component analysis in Finance". Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-264205.
Texto completoWedlake, Ryan Stuart. "Robust principal component analysis biplots". Thesis, Link to the online version, 2008. http://hdl.handle.net/10019/929.
Texto completoBrennan, Victor L. "Principal component analysis with multiresolution". [Gainesville, Fla.] : University of Florida, 2001. http://etd.fcla.edu/etd/uf/2001/ank7079/brennan%5Fdissertation.pdf.
Texto completoTitle from first page of PDF file. Document formatted into pages; contains xi, 124 p.; also contains graphics. Vita. Includes bibliographical references (p. 120-123).
Cadima, 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.
Texto completoIsaac, 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.
Texto completoDoctorat 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.
Texto completoThe 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.
Roveroni, Alessandro <1997>. "Principal Component Analysis on ESG data". Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/19941.
Texto completoBurka, Zak. "Perceptual audio classification using principal component analysis /". Online version of thesis, 2010. http://hdl.handle.net/1850/12247.
Texto completoPatak, Zdenek. "Robust principal component analysis via projection pursuit". Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/29737.
Texto completoScience, Faculty of
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.
Texto completoNilsson, Jakob y 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.
Texto completoDauwe, Alexander. "Principal component analysis of the yield curve". Master's thesis, NSBE - UNL, 2009. http://hdl.handle.net/10362/9439.
Texto completoThis 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.
Texto completoLi, Liubo Li. "Trend-Filtered Projection for Principal Component Analysis". The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1503277234178696.
Texto completoBroadbent, Lane David. "Recognition of Infrastructure Events Using Principal Component Analysis". BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/6197.
Texto completoKhwambala, 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.
Texto completoIncludes 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.
Chen, Shaokang. "Robust discriminative principal component analysis for face recognition /". [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18934.pdf.
Texto completoDimitrov, Darko [Verfasser]. "Geometric applications of principal component analysis / Darko Dimitrov". Berlin : Freie Universität Berlin, 2009. http://d-nb.info/102346392X/34.
Texto completoBinongo, Jose Nilo G. "Stylometry and its implementation by principal component analysis". Thesis, University of Ulster, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.311585.
Texto completoTan, Murat Hasan. "Principal component analysis for signal-based system identification". Thesis, University of Southampton, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.430735.
Texto completoKharva, Mohamed. "Monitoring of froth systems using principal component analysis". Thesis, Stellenbosch : Stellenbosch University, 2002. http://hdl.handle.net/10019.1/52945.
Texto completoENGLISH ABSTRACT: Flotation is notorious for its susceptibility to process upsets and consequently its poor performance, making successful flotation control systems an elusive goal. The control of industrial flotation plants is often based en the visual appearance of the froth phase, and depends to a large extent on the experience and ability of a human operator. Machine vision systems provide a novel solution to several of the problems encountered in conventional flotation systems for monitoring and control. The rapid development in computer VISIon, computational resources and artificial intelligence and the integration of these technologies are creating new possibilities in the design and implementation of commercial machine vision systems for the monitoring and control of flotation plants. Current machine vision systems are available but not without their shortcomings. These systems cannot deal with fine froths where the bubbles are very small due to the segmentation techniques employed by them. These segmentation techniques are cumbersome and computationally expensive making them slow in real time operation. The approach followed in this work uses neural networks to solve the problems mentioned above. Neural networks are able to extract information from images of the froth phase without regard to the type and structure of the froth. The parallel processing capability of neural networks, ease of implementation and the advantages of supervised or unsupervised training of neural networks make them potentially suited for real-time industrial machine vision systems. In principle, neural network models can be implemented in an adaptive manner, so that changes in the characteristics of processes are taken into account. This work documents the development of linear and non-linear principal component models, which can be used in a real-time machine vision system for the monitoring, and control of froth flotation systems. Features from froth images of flotation processes were extracted via linear and non-linear principal component analysis. Conventional linear principal component analysis and three layer autoassociative neural networks were used in the extraction of linear principal components from froth images. Non-linear principal components were extracted from froth images by a three and five layer autoassociative neural network, as well as localised principal component analysis based on k-means clustering. Three principal components were extracted for each image. The correlation coefficient was used as a measure of the amount of variance captured by each principal component. The principal components were used to classify the froth images. A probabilistic neural network and a feedforward neural network classifier were developed for the classification of the froth images. Multivariate statistical process control models were developed using the linear and non-linear principal component models. Hotellings T2 statistic and the squared prediction error based on linear and non-linear principal component models were used in the development of multivariate control charts. It was found that the first three features extracted with autoassociative neural networks were able to capture more variance in froth images than conventional linear principal components, the features extracted by the five layer autoassociative neural networks were able to classify froth images more accurately than features extracted by conventional linear principal component analysis and three layer autoassociative neural networks. As applied, localised principal component analysis proved to be ineffective, owing to difficulties with the clustering of the high dimensional image data. Finally the use of multivariate statistical process control models to detect deviations from normal plant operations are discussed and it is shown that Hotellings T2 and squared prediction error control charts are able to clearly identify non-conforming plant behaviour.
AFRIKAANSE OPSOMMING: Flottasie is berug daarvoor dat dit vatbaar vir prosesversteurings is en daarom dikwels nie na wense presteer nie. Suksesvolle flottasiebeheerstelsels bly steeds 'n ontwykende doelwit. Die beheer van nywerheidsflottasie-aanlegte word dikwels gebaseer op die visuele voorkoms van die skuimfase en hang tot 'n groot mate af van die ervaring en vaardighede van die menslike operateur. Masjienvisiestelsels voorsien 'n vindingryke oplossing tot verskeie van die probleme wat voorkom by konvensionele flottasiestelsels ten opsigte van monitering en beheer. Die vinnige ontwikkeling van rekenaarbeheerde visie, rekenaarverwante hulpbronne en kunsmatige intelligensie, asook die integrasie van hierdie tegnologieë, skep nuwe moontlikhede in die ontwerp en inwerkingstelling van kommersiële masjienvisiestelsels om flottasie-aanlegte te monitor en te beheer. Huidige masjienvisiestelsels is wel beskikbaar, maar is nie sonder tekortkominge nie. Hierdie stelsels kan nie fyn skuim hanteer nie, waar die borreltjies baie klein is as gevolg van die segmentasietegnieke wat hulle aanwend. Hierdie segmentasietegnieke is omslagtig en rekenaargesproke duur, wat veroorsaak dat dit stadig in reële tyd-aanwendings is. Die benadering wat in hierdie werk gevolg is, wend neurale netwerke aan om die bovermelde probleme op te los. Neurale netwerke is instaat om inligting te onttrek uit beelde van die skuimfase sonder om ag te slaan op die tipe en struktuur van die skuim. Die parallelle prosesseringsvermoëns van neurale netwerke, die gemak van implementering en die voordele van die opleiding van neurale netwerke met of sonder toesig maak hulle potensieel nuttig as reële tydverwante industriële masjienvisiestelsels. In beginsel kan neurale netwerke op 'n aanpassende wyse geïmplementeer word, sodat veranderinge in die kenmerke van die prosesse deurlopend in aanmerking geneem word. Kenmerke van die beelde van die skuim tydens die flottasieproses is verkry by wyse van lineêre en nie-lineêre hootkomponentsanalise. Konvensionele lineêre hoofkomponentsanalise en drie-laag outo-assosiatiewe neurale netwerke is gebruik in die onttrekking van lineêre hoofkomponente uit die beelde van die skuim. Nie-lineêre hoofkomponente is uit die beelde van die skuim onttrek by wyse van 'n drie- en vyf-laag outo-assosiatiewe neurale netwerk, asook deur 'n gelokaliseerde hoofkomponentsanalise wat op k-gemiddelde trosanalise gebaseer is. Drie hoofkomponente is vir elke beeld onttrek. Die korrelasiekoëffisiënt is gebruik as 'n maatstaf van die afwyking wat deur elke hoofkomponent aangetoon is. Die hoofkomponente is gebruik om die beelde van die skuim te klassifiseer. 'n Probalistiese neurale netwerk en 'n voorwaarts voerende neurale netwerk is vir die klassifisering van die beelde van die skuim ontwerp. Multiveranderlike statistiese prosesbeheermodelle is ontwerp met die gebruik van die lineêre en nie-lineêre hoofkomponentmodelle. Hotelling se T2 statistiek en die gekwadreerde voorspellingsfout, gebaseer op lineêre en nie-lineêre hoofkomponentmodelle, is gebruik in die ontwikkeling van multiveranderlike kontrolekaarte. Dit is gevind dat die eerste drie eienskappe wat met behulp van die outo-assosiatiewe neurale netwerke onttrek is, instaat was om meer variansie by beelde van skuim vas te vang as konvensionele lineêre hoofkomponente. Die eienskappe wat deur die vyf-laag outo-assosiatiewe neurale netwerke onttrek is, was instaat om beelde van skuim akkurater te klassifiseer as daardie eienskappe wat by wyse van konvensionele lineêre hoofkomponentanalalise en drie-laag outo-assosiatiewe neurale netwerke onttrek is. Soos toegepas, het dit geblyk dat gelokaliseerde hoofkomponentsanalise nie effektief is nie, as gevolg van die probleme rondom die trosanalise van die hoë-dimensionele beelddata. Laastens word die aanwending van multiveranderlike statistiese prosesbeheermodelle, om afwykings in normale aanlegoperasies op te spoor, bespreek. Dit word aangetoon dat Hotelling se T2 statistiek en gekwadreerdevoorspellingsfoutbeheerkaarte instaat is om afwykende aanlegwerksverrigting duidelik aan te dui.
See, Kyoungah. "Three-mode principal component analysis in designed experiments". Diss., Virginia Tech, 1993. http://hdl.handle.net/10919/40079.
Texto completoAl-Kandari, Noriah Mohammed. "Variable selection and interpretation in principal component analysis". Thesis, University of Aberdeen, 1998. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU067766.
Texto completoLi, Xiaomeng. "Human Promoter Recognition Based on Principal Component Analysis". Thesis, The University of Sydney, 2008. http://hdl.handle.net/2123/3656.
Texto completoLi, Xiaomeng. "Human Promoter Recognition Based on Principal Component Analysis". University of Sydney, 2008. http://hdl.handle.net/2123/3656.
Texto completoThis thesis presents an innovative human promoter recognition model HPR-PCA. Principal component analysis (PCA) is applied on context feature selection DNA sequences and the prediction network is built with the artificial neural network (ANN). A thorough literature review of all the relevant topics in the promoter prediction field is also provided. As the main technique of HPR-PCA, the application of PCA on feature selection is firstly developed. In order to find informative and discriminative features for effective classification, PCA is applied on the different n-mer promoter and exon combined frequency matrices, and principal components (PCs) of each matrix are generated to construct the new feature space. ANN built classifiers are used to test the discriminability of each feature space. Finally, the 3 and 5-mer feature matrix is selected as the context feature in this model. Two proposed schemes of HPR-PCA model are discussed and the implementations of sub-modules in each scheme are introduced. The context features selected by PCA are III used to build three promoter and non-promoter classifiers. CpG-island modules are embedded into models in different ways. In the comparison, Scheme I obtains better prediction results on two test sets so it is adopted as the model for HPR-PCA for further evaluation. Three existing promoter prediction systems are used to compare to HPR-PCA on three test sets including the chromosome 22 sequence. The performance of HPR-PCA is outstanding compared to the other four systems.
Khawaja, Antoun. "Automatic ECG analysis using principal component analysis and wavelet transformation". Karlsruhe Univ.-Verl. Karlsruhe, 2007. http://www.uvka.de/univerlag/volltexte/2007/227/.
Texto completoSchmid, Martin. "Anwendung der Principal Component Analysis auf die Commodity-Preise". St. Gallen, 2007. http://www.biblio.unisg.ch/org/biblio/edoc.nsf/wwwDisplayIdentifier/02282663001/$FILE/02282663001.pdf.
Texto completoSkittides, Christina. "Statistical modelling of wind energy using Principal Component Analysis". Thesis, Heriot-Watt University, 2015. http://hdl.handle.net/10399/2930.
Texto completoShannak, Kamal Majed. "On Non-Linear Principal Component Analysis for Process Monitoring". Fogler Library, University of Maine, 2004. http://www.library.umaine.edu/theses/pdf/ShannakKM2004.pdf.
Texto completoABDELWAHAB, MOATAZ MAHMOUD. "NOVEL FACIAL IMAGE RECOGNITION TECHNIQUES EMPLOYING PRINCIPAL COMPONENT ANALYSIS". Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2181.
Texto completoPh.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering PhD
Ragozzine, Brett A. "Modeling the Point Spread Function Using Principal Component Analysis". Ohio University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1224684806.
Texto completoBrock, James L. "Acoustic classification using independent component analysis /". Link to online version, 2006. https://ritdml.rit.edu/dspace/handle/1850/2067.
Texto completoChivers, Daniel Stephen. "Human Action Recognition by Principal Component Analysis of Motion Curves". Wright State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=wright1353374113.
Texto completoTeixeira, 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/.
Texto completoThe 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.
Cao, Zisheng y 曹子晟. "Incremental algorithms for multilinear principal component analysis of tensor objects". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hdl.handle.net/10722/208151.
Texto completopublished_or_final_version
Industrial and Manufacturing Systems Engineering
Doctoral
Doctor of Philosophy
Roy, 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/.
Texto completoSöderström, Ulrik. "Very Low Bitrate Video Communication : A Principal Component Analysis Approach". Doctoral thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-1808.
Texto completoVerdebout, Thomas. "Optimal inference for one-sample and multisample principal component analysis". Doctoral thesis, Universite Libre de Bruxelles, 2008. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210448.
Texto completoA ce jour, les méthodes d'inférence utilisées en Analyse en Composantes Principales par les praticiens sont généralement fondées sur l'hypothèse de normalité des observations. Hypothèse qui peut, dans bien des situations, être remise en question.
Le but de ce travail est de construire des procédures de test pour l'Analyse en Composantes Principales qui soient valides sous une famille plus importante de lois de probabilité, la famille des lois elliptiques. Pour ce faire, nous utilisons la méthodologie de Le Cam combinée au principe d'invariance. Ce dernier stipule que si une hypothèse nulle reste invariante sous l'action d'un groupe de transformations, alors, il faut se restreindre à des statistiques de test également invariantes sous l'action de ce groupe. Toutes les hypothèses nulles associées aux problèmes considérés dans ce travail sont invariantes sous l'action d'un groupe de transformations appellées monotones radiales. L'invariant maximal associé à ce groupe est le vecteur des signes multivariés et des rangs des distances de Mahalanobis entre les observations et l'origine.
Les paramètres d'intérêt en Analyse en composantes Principales sont les vecteurs propres et valeurs propres de matrices définies positives. Ce qui implique que l'espace des paramètres n'est pas linéaire. Nous développons donc une manière d'obtenir des procédures optimales pour des suite d'experiences locales courbées.
Les statistiques de test introduites sont optimales au sens de Le Cam et mesurables en l'invariant maximal décrit ci-dessus.
Les procédures de test basées sur ces statistiques possèdent de nombreuses propriétés attractives: elles sont valides sous la famille des lois elliptiques, elles sont efficaces sous une densité spécifiée et possèdent de très bonnes efficacités asymptotiques relatives par rapport à leurs concurrentes. En particulier, lorsqu'elles sont basées sur des scores Gaussiens, elles sont aussi efficaces que les procédures Gaussiennes habituelles et sont bien plus efficaces que ces dernières si l'hypothèse de normalité des observations n'est pas remplie.
Doctorat en Sciences
info:eu-repo/semantics/nonPublished
Harasti, Paul Robert. "Hurricane properties by principal component analysis of Doppler radar data". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq53836.pdf.
Texto completoYang, Libin. "An Application of Principal Component Analysis to Stock Portfolio Management". Thesis, University of Canterbury. Department of economics and finance, 2015. http://hdl.handle.net/10092/10293.
Texto completoWu, Rui. "A comparison study of principal component analysis and nonlinear principal component analysis". 2007. http://etd.lib.fsu.edu/theses/available/etd-04042007-191940.
Texto completoAdvisor: Jerry F. Magnan, Florida State University, College of Arts and Sciences, Dept. of Mathematics. Title and description from dissertation home page (viewed July 12, 2007). Document formatted into pages; contains xi, 68 pages. Includes bibliographical references.
Kurylowicz, Martin. "Principal Component Analysis of Gramicidin". Thesis, 2010. http://hdl.handle.net/1807/24790.
Texto completoWijnen, Michael. "Online Tensor Robust Principal Component Analysis". Thesis, 2018. http://hdl.handle.net/1885/170630.
Texto completoTseng, Chi-Chieh y 鄭期傑. "Earthquake Detection By Principal Component Analysis". Thesis, 2016. http://ndltd.ncl.edu.tw/handle/88853561622128415704.
Texto completo國立臺灣科技大學
資訊工程系
104
Earthquake is a major disaster in many countries, and its effect can be devastating. Due to the challenges in predicting earthquakes, researchers have turned their attention to detecting the occurrence of an earthquake as soon as possible, a concept known as earthquake early warning (EEW). In this paper, we propose a novel method for detecting earthquakes based on Principal Component Analysis (PCA), built upon the Palert seismic sensor network in Taiwan. By building statistical models for the behavior of the network, we can better understand the behavior during of the noise, allowing us to separate an earthquake from the constant false alarms. Experiment results with real world data show that our method can detect earthquakes earlier than existing methods without increase in false alarm rate or decrease in detection rate, which is pivotal in ensuring the credibility and effectiveness of the system. Our system is ready for real world deployment, and can potentially save lives and prevent property damage caused by earthquakes.
Chao, Hsiang-Chi y 趙湘琪. "3-way data principal component analysis". Thesis, 1995. http://ndltd.ncl.edu.tw/handle/39703156662028311670.
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