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Tesis sobre el tema "Dimensionality reduction"

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Publicado: 4 de junio de 2021
Última modificación: 25 de julio de 2025

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1

Ariu, Kaito. "Online Dimensionality Reduction." Licentiate thesis, KTH, Reglerteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-290791.

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In this thesis, we investigate online dimensionality reduction methods, wherethe algorithms learn by sequentially acquiring data. We focus on two specificalgorithm design problems in (i) recommender systems and (ii) heterogeneousclustering from binary user feedback. (i) For recommender systems, we consider a system consisting of m users and n items. In each round, a user,selected uniformly at random, arrives to the system and requests a recommendation. The algorithm observes the user id and recommends an itemfrom the item set. A notable restriction here is that the same item cannotbe recommend
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2

LEGRAMANTI, SIRIO. "Bayesian dimensionality reduction." Doctoral thesis, Università Bocconi, 2021. http://hdl.handle.net/11565/4035711.

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No abstract available<br>We are currently witnessing an explosion in the amount of available data. Such growth involves not only the number of data points but also their dimensionality. This poses new challenges to statistical modeling and computations, thus making dimensionality reduction more central than ever. In the present thesis, we provide methodological, computational and theoretical advancements in Bayesian dimensionality reduction via novel structured priors. Namely, we develop a new increasing shrinkage prior and illustrate how it can be employed to discard redundant dimensions in G
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3

Baldiwala, Aliakbar. "Dimensionality Reduction for Commercial Vehicle Fleet Monitoring." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/38330.

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A variety of new features have been added in the present-day vehicles like a pre-crash warning, the vehicle to vehicle communication, semi-autonomous driving systems, telematics, drive by wire. They demand very high bandwidth from in-vehicle networks. Various electronic control units present inside the automotive transmit useful information via automotive multiplexing. Automotive multiplexing allows sharing information among various intelligent modules inside an automotive electronic system. Optimum functionality is achieved by transmitting this data in real time. The high bandwidth and high-
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4

Bolelli, Maria Virginia. "Diffusion Maps for Dimensionality Reduction." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18246/.

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In this thesis we present the diffusion maps, a framework based on diffusion processes for finding meaningful geometric descriptions of data sets. A diffusion process can be described via an iterative application of the heat kernel which has two main characteristics: it satisfies a Markov semigroup property and its level sets encode all geometric features of the space. This process, well known in regular manifolds, has been extended to general data set by Coifman and Lafon. They define a diffusion kernel starting from the geometric properties of the data and their density properties. This kern
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5

Khosla, Nitin, and n/a. "Dimensionality Reduction Using Factor Analysis." Griffith University. School of Engineering, 2006. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20061010.151217.

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In many pattern recognition applications, a large number of features are extracted in order to ensure an accurate classification of unknown classes. One way to solve the problems of high dimensions is to first reduce the dimensionality of the data to a manageable size, keeping as much of the original information as possible and then feed the reduced-dimensional data into a pattern recognition system. In this situation, dimensionality reduction process becomes the pre-processing stage of the pattern recognition system. In addition to this, probablility density estimation, with fewer variables i
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6

Vamulapalli, Harika Rao. "On Dimensionality Reduction of Data." ScholarWorks@UNO, 2010. http://scholarworks.uno.edu/td/1211.

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Random projection method is one of the important tools for the dimensionality reduction of data which can be made efficient with strong error guarantees. In this thesis, we focus on linear transforms of high dimensional data to the low dimensional space satisfying the Johnson-Lindenstrauss lemma. In addition, we also prove some theoretical results relating to the projections that are of interest when applying them in practical applications. We show how the technique can be applied to synthetic data with probabilistic guarantee on the pairwise distance. The connection between dimensionality red
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7

Widemann, David P. "Dimensionality reduction for hyperspectral data." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8448.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2008.<br>Thesis research directed by: Dept. of Mathematics. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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8

Khosla, Nitin. "Dimensionality Reduction Using Factor Analysis." Thesis, Griffith University, 2006. http://hdl.handle.net/10072/366058.

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In many pattern recognition applications, a large number of features are extracted in order to ensure an accurate classification of unknown classes. One way to solve the problems of high dimensions is to first reduce the dimensionality of the data to a manageable size, keeping as much of the original information as possible and then feed the reduced-dimensional data into a pattern recognition system. In this situation, dimensionality reduction process becomes the pre-processing stage of the pattern recognition system. In addition to this, probablility density estimation, with fewer variables i
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9

Sætrom, Jon. "Reduction of Dimensionality in Spatiotemporal Models." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for matematiske fag, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-11247.

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10

Ghodsi, Boushehri Ali. "Nonlinear Dimensionality Reduction with Side Information." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/1020.

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In this thesis, I look at three problems with important applications in data processing. Incorporating side information, provided by the user or derived from data, is a main theme of each of these problems. <br /><br /> This thesis makes a number of contributions. The first is a technique for combining different embedding objectives, which is then exploited to incorporate side information expressed in terms of transformation invariants known to hold in the data. It also introduces two different ways of incorporating transformation invariants in order to make new similarity meas
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11

Merola, Giovanni Maria. "Dimensionality reduction methods in multivariate prediction." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0022/NQ32847.pdf.

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12

Musco, Cameron N. (Cameron Nicholas). "Dimensionality reduction for k-means clustering." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/101473.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 123-131).<br>In this thesis we study dimensionality reduction techniques for approximate k-means clustering. Given a large dataset, we consider how to quickly compress to a smaller dataset (a sketch), such that solving the k-me
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13

Law, Hiu Chung. "Clustering, dimensionality reduction, and side information." Diss., Connect to online resource - MSU authorized users, 2006.

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Thesis (Ph. D.)--Michigan State University. Dept. of Computer Science & Engineering, 2006.<br>Title from PDF t.p. (viewed on June 19, 2009) Includes bibliographical references (p. 296-317). Also issued in print.
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14

Vasiloglou, Nikolaos. "Isometry and convexity in dimensionality reduction." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28120.

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Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2009.<br>Committee Chair: David Anderson; Committee Co-Chair: Alexander Gray; Committee Member: Anthony Yezzi; Committee Member: Hongyuan Zha; Committee Member: Justin Romberg; Committee Member: Ronald Schafer.
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15

Gagliardi, Alessandro <1990&gt. "Dimensionality reduction methods for paleoclimate reconstructions." Master's Degree Thesis, Università Ca' Foscari Venezia, 2017. http://hdl.handle.net/10579/10434.

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Paleoclimatology seeks to understand past changes in climate occurred before the instrumental period through paleoclimate archives. These archives consist of natural materials that keep trace of climate changes with different time scales and resolutions. Tree-ring archives are able to provide a timescale of thousands of years with annual resolution. This thesis discusses reconstruction of the past temperature in the period ranging from year 1400 until 1849 on the basis of the information available in a tree-ring dataset consisting of 70 trees located in the United States of America. The temper
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16

Musco, Christopher Paul. "Dimensionality reduction for sparse and structured matrices." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/99856.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 97-103).<br>Dimensionality reduction has become a critical tool for quickly solving massive matrix problems. Especially in modern data analysis and machine learning applications, an overabundance of data features or examples can make it impossible to apply standard algorithms efficiently. To address this issue, it is often possible to distill data to a much smaller set of informative features
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17

Beach, David J. "Anomaly Detection with Advanced Nonlinear Dimensionality Reduction." Digital WPI, 2020. https://digitalcommons.wpi.edu/etd-theses/1378.

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Dimensionality reduction techniques such as t-SNE and UMAP are useful both for overview of high-dimensional datasets and as part of a machine learning pipeline. These techniques create a non-parametric model of the manifold by fitting a density kernel about each data point using the distances to its k-nearest neighbors. In dense regions, this approach works well, but in sparse regions, it tends to draw unrelated points into the nearest cluster. Our work focuses on a homotopy method which imposes graph-based regularization over the manifold parameters to update the embedding. As the homotopy pa
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18

DWIVEDI, SAURABH. "DIMENSIONALITY REDUCTION FOR DATA DRIVEN PROCESS MODELING." University of Cincinnati / OhioLINK, 2003. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1069770129.

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19

XU, NUO. "AGGRESSIVE DIMENSIONALITY REDUCTION FOR DATA-DRIVEN MODELING." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1178640357.

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20

Welshman, Christopher. "Dimensionality reduction for dynamical systems with parameters." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/dimensionality-reduction-for-dynamical-systems-with-parameters(69dab7de-b1dd-4d74-901f-61e02decf16a).html.

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Dimensionality reduction methods allow for the study of high-dimensional systems by producing low-dimensional descriptions that preserve the relevant structure and features of interest. For dynamical systems, attractors are particularly important examples of such features, as they govern the long-term dynamics of the system, and are typically low-dimensional even if the state space is high- or infinite-dimensional. Methods for reduction need to be able to determine a suitable reduced state space in which to describe the attractor, and to produce a reduced description of the corresponding dynam
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21

Chang, Kui-yu. "Nonlinear dimensionality reduction using probabilistic principal surfaces /." Digital version accessible at:, 2000. http://wwwlib.umi.com/cr/utexas/main.

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22

Tosi, Alessandra. "Visualization and interpretability in probabilistic dimensionality reduction models." Doctoral thesis, Universitat Politècnica de Catalunya, 2014. http://hdl.handle.net/10803/285013.

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Over the last few decades, data analysis has swiftly evolved from being a task addressed mainly within the remit of multivariate statistics, to an endevour in which data heterogeneity, complexity and even sheer size, driven by computational advances, call for alternative strategies, such as those provided by pattern recognition and machine learning. Any data analysis process aims to extract new knowledge from data. Knowledge extraction is not a trivial task and it is not limited to the generation of data models or the recognition of patterns. The use of machine learning techniques for multiva
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23

Guo, Hong. "Feature generation and dimensionality reduction using genetic programming." Thesis, University of Liverpool, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.511054.

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24

Kalamaras, Ilias. "A novel approach for multimodal graph dimensionality reduction." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/42224.

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This thesis deals with the problem of multimodal dimensionality reduction (DR), which arises when the input objects, to be mapped on a low-dimensional space, consist of multiple vectorial representations, instead of a single one. Herein, the problem is addressed in two alternative manners. One is based on the traditional notion of modality fusion, but using a novel approach to determine the fusion weights. In order to optimally fuse the modalities, the known graph embedding DR framework is extended to multiple modalities by considering a weighted sum of the involved affinity matrices. The weig
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25

Le, Moan Steven. "Dimensionality reduction and saliency for spectral image visualization." Phd thesis, Université de Bourgogne, 2012. http://tel.archives-ouvertes.fr/tel-00825495.

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Nowadays, digital imaging is mostly based on the paradigm that a combinations of a small number of so-called primary colors is sufficient to represent any visible color. For instance, most cameras usepixels with three dimensions: Red, Green and Blue (RGB). Such low dimensional technology suffers from several limitations such as a sensitivity to metamerism and a bounded range of wavelengths. Spectral imaging technologies offer the possibility to overcome these downsides by dealing more finely withe the electromagnetic spectrum. Mutli-, hyper- or ultra-spectral images contain a large number of c
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26

Bitzer, Sebastian. "Nonlinear dimensionality reduction for motion synthesis and control." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/4869.

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Synthesising motion of human character animations or humanoid robots is vastly complicated by the large number of degrees of freedom in their kinematics. Control spaces become so large, that automated methods designed to adaptively generate movements become computationally infeasible or fail to find acceptable solutions. In this thesis we investigate how demonstrations of previously successful movements can be used to inform the production of new movements that are adapted to new situations. In particular, we evaluate the use of nonlinear dimensionality reduction techniques to find compact rep
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27

Ross, Ian. "Nonlinear dimensionality reduction methods in climate data analysis." Thesis, University of Bristol, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.492479.

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Linear dimensionality reduction techniques, notably principal component analysis, are widely used in climate data analysis as a means to aid in the interpretation of datasets of high dimensionality. These hnear methods may not be appropriate for the analysis of data arising from nonlinear processes occurring in the climate system. Numerous techniques for nonlinear dimensionality reduction have been developed recently that may provide a potentially useful tool for the identification of low-dimensional manifolds in climate data sets arising from nonlinear dynamics. In this thesis I apply three s
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28

Bourrier, Anthony. "Compressed sensing and dimensionality reduction for unsupervised learning." Phd thesis, Université Rennes 1, 2014. http://tel.archives-ouvertes.fr/tel-01023030.

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Cette thèse est motivée par la perspective de rapprochement entre traitement du signal et apprentissage statistique, et plus particulièrement par l'exploitation de techniques d'échantillonnage compressé afin de réduire le coût de tâches d'apprentissage. Après avoir rappelé les bases de l'échantillonnage compressé et mentionné quelques techniques d'analyse de données s'appuyant sur des idées similaires, nous proposons un cadre de travail pour l'estimation de paramètres de mélange de densités de probabilité dans lequel les données d'entraînement sont compressées en une représentation de taille f
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29

Shekhar, Karthik. "Dimensionality reduction in immunology : from viruses to cells." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/98339.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, February 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 301-318).<br>Developing successful prophylactic and therapeutic strategies against infections of RNA viruses like HIV requires a combined understanding of the evolutionary constraints of the virus, as well as of the immunologic determinants associated with effective viremic control. Recent technologies enable viral and immune parameters to be measured at an unprecedented scale and resolution across multi
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30

Payne, Terry R. "Dimensionality reduction and representation for nearest neighbour learning." Thesis, University of Aberdeen, 1999. https://eprints.soton.ac.uk/257788/.

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An increasing number of intelligent information agents employ Nearest Neighbour learning algorithms to provide personalised assistance to the user. This assistance may be in the form of recognising or locating documents that the user might find relevant or interesting. To achieve this, documents must be mapped into a representation that can be presented to the learning algorithm. Simple heuristic techniques are generally used to identify relevant terms from the documents. These terms are then used to construct large, sparse training vectors. The work presented here investigates an alternative
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31

Hui, Shirley. "FlexSADRA: Flexible Structural Alignment using a Dimensionality Reduction Approach." Thesis, University of Waterloo, 2005. http://hdl.handle.net/10012/1173.

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A topic of research that is frequently studied in Structural Biology is the problem of determining the degree of similarity between two protein structures. The most common solution is to perform a three dimensional structural alignment on the two structures. Rigid structural alignment algorithms have been developed in the past to accomplish this but treat the protein molecules as immutable structures. Since protein structures can bend and flex, rigid algorithms do not yield accurate results and as a result, flexible structural alignment algorithms have been developed. The problem wit
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32

Donnelly, Mark Patrick. "Classification of body surface potential maps through dimensionality reduction." Thesis, University of Ulster, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.516131.

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33

Jayaraman, Gautam 1981. "Applying a randomized nearest neighbors algorithm to dimensionality reduction." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/29665.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.<br>Includes bibliographical references (p. 95-96).<br>In this thesis, I implemented a randomized nearest neighbors algorithm in order to optimize an existing dimensionality reduction algorithm. In implementation I resolved details that were not considered in the design stage, and optimized the nearest neighbor system for use by the dimensionality reduction system. By using the new nearest neighbor system as a subroutine, the dimensionality reduction system runs in time O(n log n)
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34

Ray, Sujan. "Dimensionality Reduction in Healthcare Data Analysis on Cloud Platform." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin161375080072697.

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35

Ha, Sook Shin. "Dimensionality Reduction, Feature Selection and Visualization of Biological Data." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/77169.

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Due to the high dimensionality of most biological data, it is a difficult task to directly analyze, model and visualize the data to gain biological insight. Thus, dimensionality reduction becomes an imperative pre-processing step in analyzing and visualizing high-dimensional biological data. Two major approaches to dimensionality reduction in genomic analysis and biomarker identification studies are: Feature extraction, creating new features by combining existing ones based on a mapping technique; and feature selection, choosing an optimal subset of all features based on an objective function.
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36

Sharma, Vikas Manesh. "AN EVALUATION OF DIMENSIONALITY REDUCTION ON CELL FORMATION EFFICACY." Ohio University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1174503824.

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37

Strong, Stephen. "Dimensionality Reduction for the Purposes of Automatic Pattern Classification." Thesis, Griffith University, 2013. http://hdl.handle.net/10072/367333.

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Pattern classification is a common technique used in a variety of applications. From simple tasks, such as password acceptance, to more complex tasks, such as identication by biometrics, speech recognition, and text recognition. As a result, a large number of pattern classification algorithms have emerged, allowing computers to perform these tasks. However, these techniques become less eective when excessive data on a given object is provided in comparison to the number of samples required to train. As a result, much research has been placed in nding ecient methods of reducing the dimensionali
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38

Moraes, Lailson Bandeira de. "Two-dimensional extensions of semi-supervised dimensionality reduction methods." Universidade Federal de Pernambuco, 2013. https://repositorio.ufpe.br/handle/123456789/12388.

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Submitted by João Arthur Martins (joao.arthur@ufpe.br) on 2015-03-11T18:17:21Z No. of bitstreams: 2 Dissertaçao Lailson de Moraes.pdf: 4634910 bytes, checksum: cbec580f8cbc24cb3feb2379a1d2dfbd (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5)<br>Approved for entry into archive by Daniella Sodre (daniella.sodre@ufpe.br) on 2015-03-13T13:02:06Z (GMT) No. of bitstreams: 2 Dissertaçao Lailson de Moraes.pdf: 4634910 bytes, checksum: cbec580f8cbc24cb3feb2379a1d2dfbd (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5)<br>Made available in
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39

Najim, S. A. "Faithful visualization and dimensionality reduction on graphics processing unit." Thesis, Bangor University, 2014. https://research.bangor.ac.uk/portal/en/theses/faithful-visualization-and-dimensionality-reduction-on-graphics-processing-unit(527800f6-191c-4257-98d1-7909a1ab9ead).html.

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40

Gashler, Michael S. "Advancing the Effectiveness of Non-Linear Dimensionality Reduction Techniques." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3216.

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Data that is represented with high dimensionality presents a computational complexity challenge for many existing algorithms. Limiting dimensionality by discarding attributes is sometimes a poor solution to this problem because significant high-level concepts may be encoded in the data across many or all of the attributes. Non-linear dimensionality reduction (NLDR) techniques have been successful with many problems at minimizing dimensionality while preserving intrinsic high-level concepts that are encoded with varying combinations of attributes. Unfortunately, many challenges remain with exis
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41

Li, Ye. "MULTIFACTOR DIMENSIONALITY REDUCTION WITH P RISK SCORES PER PERSON." UKnowledge, 2018. https://uknowledge.uky.edu/statistics_etds/34.

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After reviewing Multifactor Dimensionality Reduction(MDR) and its extensions, an approach to obtain P(larger than 1) risk scores is proposed to predict the continuous outcome for each subject. We study the mean square error(MSE) of dimensionality reduced models fitted with sets of 2 risk scores and investigate the MSE for several special cases of the covariance matrix. A methodology is proposed to select a best set of P risk scores when P is specified a priori. Simulation studies based on true models of different dimensions(larger than 3) demonstrate that the selected set of P(larger than 1) r
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42

Di, Ciaccio Lucio. "Feature selection and dimensionality reduction for supervised data analysis." Thesis, Massachusetts Institute of Technology, 2016. https://hdl.handle.net/1721.1/122827.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2016<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 103-106).<br>by Lucio Di Ciaccio.<br>S.M.<br>S.M. Massachusetts Institute of Technology, Department of Aeronautics and Astronautics
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43

Atkison, Travis Levestis. "Using random projections for dimensionality reduction in identifying rogue applications." Diss., Mississippi State : Mississippi State University, 2009. http://library.msstate.edu/etd/show.asp?etd=etd-04032009-133701.

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44

Gámez, López Antonio Juan. "Application of nonlinear dimensionality reduction to climate data for prediction." [S.l.] : [s.n.], 2006. http://opus.kobv.de/ubp/volltexte/2006/1095.

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45

Colomé, Figueras Adrià. "Bimanual robot skills: MP encoding, dimensionality reduction and reinforcement learning." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/586163.

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In our culture, robots have been in novels and cinema for a long time, but it has been specially in the last two decades when the improvements in hardware - better computational power and components - and advances in Artificial Intelligence (AI), have allowed robots to start sharing spaces with humans. Such situations require, aside from ethical considerations, robots to be able to move with both compliance and precision, and learn at different levels, such as perception, planning, and motion, being the latter the focus of this work. The first issue addressed in this thesis is inverse kinemat
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46

Kharal, Rosina. "Semidefinite Embedding for the Dimensionality Reduction of DNA Microarray Data." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2945.

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Harnessing the power of DNA microarray technology requires the existence of analysis methods that accurately interpret microarray data. Current literature abounds with algorithms meant for the investigation of microarray data. However, there is need for an efficient approach that combines different techniques of microarray data analysis and provides a viable solution to dimensionality reduction of microarray data. Reducing the high dimensionality of microarray data is one approach in striving to better understand the information contained within the data. We propose a novel approach fo
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47

Hira, Zena Maria. "Dimensionality reduction methods for microarray cancer data using prior knowledge." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/33812.

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Microarray studies are currently a very popular source of biological information. They allow the simultaneous measurement of hundreds of thousands of genes, drastically increasing the amount of data that can be gathered in a small amount of time and also decreasing the cost of producing such results. Large numbers of high dimensional data sets are currently being generated and there is an ongoing need to find ways to analyse them to obtain meaningful interpretations. Many microarray experiments are concerned with answering specific biological or medical questions regarding diseases and treatme
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Gorrell, Genevieve. "Generalized Hebbian Algorithm for Dimensionality Reduction in Natural Language Processing." Doctoral thesis, Linköping : Department of Computer and Information Science, Linköpings universitet, 2006. http://www.bibl.liu.se/liupubl/disp/disp2006/tek1045s.pdf.

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Boone, Gary Noel. "Extreme dimensionality reduction for text learning : cluster-generated feature spaces." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/8139.

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Gámez, López Antonio Juan. "Application of nonlinear dimensionality reduction to climate data for prediction." Phd thesis, Universität Potsdam, 2006. http://opus.kobv.de/ubp/volltexte/2006/1095/.

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This Thesis was devoted to the study of the coupled system composed by El Niño/Southern Oscillation and the Annual Cycle. More precisely, the work was focused on two main problems: 1. How to separate both oscillations into an affordable model for understanding the behaviour of the whole system. 2. How to model the system in order to achieve a better understanding of the interaction, as well as to predict future states of the system. We focused our efforts in the Sea Surface Temperature equations, considering that atmospheric effects were secondary to the ocean dynamics. The results found may b
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