Relevant bibliographies by topics / Data dimension / Dissertations / Theses
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Dissertations / Theses on the topic 'Data dimension'

Author: Grafiati
Published: 28 May 2022
Last updated: 30 July 2025

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1

Peng, Wei. "Clutter-based dimension reordering in multi-dimensional data visualization." Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-01115-222940.

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2

Boulesteix, Anne-Laure. "Dimension reduction and Classification with High-Dimensional Microarray Data." Diss., lmu, 2005. http://nbn-resolving.de/urn:nbn:de:bvb:19-28017.

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3

Samko, Oksana. "Low dimension hierarchical subspace modelling of high dimensional data." Thesis, Cardiff University, 2009. http://orca.cf.ac.uk/54883/.

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Building models of high-dimensional data in a low dimensional space has become extremely popular in recent years. Motion tracking, facial animation, stock market tracking, digital libraries and many other different models have been built and tuned to specific application domains. However, when the underlying structure of the original data is unknown, the modelling of such data is still an open question. The problem is of interest as capturing and storing large amounts of high dimensional data has become trivial, yet the capability to process, interpret, and use this data is limited. In this th
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4

Hassan, Tahir Mohammed. "Data-independent vs. data-dependent dimension reduction for pattern recognition in high dimensional spaces." Thesis, University of Buckingham, 2017. http://bear.buckingham.ac.uk/199/.

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There has been a rapid emergence of new pattern recognition/classification techniques in a variety of real world applications over the last few decades. In most of the pattern recognition/classification applications, the pattern of interest is modelled by a data vector/array of very high dimension. The main challenges in such applications are related to the efficiency of retrieval, analysis, and verifying/classifying the pattern/object of interest. The “Curse of Dimension” is a reference to these challenges and is commonly addressed by Dimension Reduction (DR) techniques. Several DR techniques
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5

Yahya, Waheed Babatunde. "Sequential Dimension Reduction and Prediction Methods with High-dimensional Microarray Data." Diss., lmu, 2009. http://nbn-resolving.de/urn:nbn:de:bvb:19-102544.

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6

XIA, QI. "Sufficient Dimension Reduction with Missing Data." Diss., Temple University Libraries, 2017. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/469880.

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Statistics<br>Ph.D.<br>Existing sufficient dimension reduction (SDR) methods typically consider cases with no missing data. The dissertation aims to propose methods to facilitate the SDR methods when the response can be missing. The first part of the dissertation focuses on the seminal sliced inverse regression (SIR) approach proposed by Li (1991). We show that missing responses generally affect the validity of the inverse regressions under the mechanism of missing at random. We then propose a simple and effective adjustment with inverse probability weighting that guarantees the validity of SI
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7

Wauters, John, and John Wauters. "Independence Screening in High-Dimensional Data." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/623083.

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High-dimensional data, data in which the number of dimensions exceeds the number of observations, is increasingly common in statistics. The term "ultra-high dimensional" is defined by Fan and Lv (2008) as describing the situation where log(p) is of order O(na) for some a in the interval (0, ½). It arises in many contexts such as gene expression data, proteomic data, imaging data, tomography, and finance, as well as others. High-dimensional data present a challenge to traditional statistical techniques. In traditional statistical settings, models have a small number of features, chosen base
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8

Battey, Heather Suzanne. "Dimension reduction and automatic smoothing in high dimensional and functional data analysis." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609849.

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9

Weng, Jiaying. "TRANSFORMS IN SUFFICIENT DIMENSION REDUCTION AND THEIR APPLICATIONS IN HIGH DIMENSIONAL DATA." UKnowledge, 2019. https://uknowledge.uky.edu/statistics_etds/40.

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The big data era poses great challenges as well as opportunities for researchers to develop efficient statistical approaches to analyze massive data. Sufficient dimension reduction is such an important tool in modern data analysis and has received extensive attention in both academia and industry. In this dissertation, we introduce inverse regression estimators using Fourier transforms, which is superior to the existing SDR methods in two folds, (1) it avoids the slicing of the response variable, (2) it can be readily extended to solve the high dimensional data problem. For the ultra-high dime
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10

Ahn, Jeongyoun Marron James Stephen. "High dimension, low sample size data analysis." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2006. http://dc.lib.unc.edu/u?/etd,375.

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Thesis (Ph. D.)--University of North Carolina at Chapel Hill, 2006.<br>Title from electronic title page (viewed Oct. 10, 2007). "... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Statistics and Operations Research." Discipline: Statistics and Operations Research; Department/School: Statistics and Operations Research.
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11

Falk, Soylu Denniz. "Dimension Reduction Methods for Predicting Financial Data." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-254573.

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12

Li, Yingxing. "On sliced methods in dimension reduction." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B31559256.

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13

Li, Yingxing, and 李迎星. "On sliced methods in dimension reduction." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B31559256.

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14

Race, Shaina L. "Data Clustering via Dimension Reduction and Algorithm Aggregation." NCSU, 2008. http://www.lib.ncsu.edu/theses/available/etd-08182008-172335/.

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We focus on the problem of clustering large textual data sets. We present 3 well-known clustering algorithms and suggest enhancements involving dimension reduction. We propose a novel method of algorithm aggregation that allows us to use many clustering algorithms at once to arrive on a single solution. This method helps stave off the inconsistency inherent in most clustering algorithms as they are applied to various data sets. We implement our algorithms on several large benchmark data sets.
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15

Enki, Doyo Gragn. "Interpretable and fast dimension reduction of multivariate data." Thesis, Open University, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.530504.

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The main objective of this thesis is to propose new techniques to simplify the interpretation of newly formed `variables' or components, while reducing the dimensionality of multivariate data. Most attention is given to the interpretation of principal components, although one chapter is devoted to that of factors in factor analysis. Sparse principal components are proposed, in which some of the component loadings are made exactly zero. One approach is to make use of the idea of correlation biplots, where orthogonal matrix of sparse loadings is obtained from computing the biplot factors of the
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16

Cosma, Ioana Ada. "Dimension reduction of streaming data via random projections." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:09eafd84-8cb3-4e54-8daf-18db7832bcfc.

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A data stream is a transiently observed sequence of data elements that arrive unordered, with repetitions, and at very high rate of transmission. Examples include Internet traffic data, networks of banking and credit transactions, and radar derived meteorological data. Computer science and engineering communities have developed randomised, probabilistic algorithms to estimate statistics of interest over streaming data on the fly, with small computational complexity and storage requirements, by constructing low dimensional representations of the stream known as data sketches. This thesis combin
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17

Berlingerio, Michele. "Graph and network data: mining the temporal dimension." Thesis, IMT Alti Studi Lucca, 2009. http://e-theses.imtlucca.it/21/1/Berlingerio_phdthesis.pdf.

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In the last years, there have been many studies on analyzing network and graph data. A wide range of problems, such as studying the global and local properties of a graph, finding interesting structures, modeling particular characteristics, assessing the properties of some particular networks such as the Web or a co-authorship networks, have increased the attention of the scientific community, involved in finding efficient and powerful techniques to enable the achievement of the desired results. For example, with the aim of finding interesting and frequent substructures in graphs, algorithms
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18

Zhao, Jianhua. "Some topics in dimension reduction and clustering." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B4284180X.

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19

Durif, Ghislain. "Multivariate analysis of high-throughput sequencing data." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSE1334/document.

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L'analyse statistique de données de séquençage à haut débit (NGS) pose des questions computationnelles concernant la modélisation et l'inférence, en particulier à cause de la grande dimension des données. Le travail de recherche dans ce manuscrit porte sur des méthodes de réductions de dimension hybrides, basées sur des approches de compression (représentation dans un espace de faible dimension) et de sélection de variables. Des développements sont menés concernant la régression "Partial Least Squares" parcimonieuse (supervisée) et les méthodes de factorisation parcimonieuse de matrices (non s
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20

Gao, Yuan. "Dimension Identification in Data Warehouse Based on Activity Theory." Thesis, Växjö University, School of Mathematics and Systems Engineering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:vxu:diva-898.

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<p>Nowadays, business intelligence techniques are applied more and more often in different settings including corporations and organizations both in the private and public sector. It is really a broad field which can assist business people to realize the state of their organization and make profitable decisions.</p><p>In this thesis, I will focus on one of its components, data warehouse, by proposing activity theory as the method to solve the dimension identification problem in data warehouse. Under the background of project IMIS and the involved personnel, who determine the dimension, firstly
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21

Wang, Jianwei. "Composing dimension and fact mappings in peer data warehouses." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27426.

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Semantic mappings are correspondences that are established between instance or schema level vocabularies of autonomous and heterogeneous data sources. The importance of semantic mappings is steadily increasing in recent data sharing architectures as these mappings enable query answering across heterogeneous boundaries. Peer data management systems (PDBMSs) are one such architecture that has semantic mappings at its core. These systems are made up of fully autonomous network nodes, called peers, which contain data sources to be shared with other peers, called acquaintances. A peer data warehous
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22

Anderson, Joseph T. "Geometric Methods for Robust Data Analysis in High Dimension." The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488372786126891.

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23

Meng, Xiaohong. "A data warehouse view selection scheme to accommodate dimension hierarchies." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0010/MQ52611.pdf.

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24

Spirko, Lauren Nicole. "Variable Selection and Supervised Dimension Reduction for Large-Scale Genomic Data with Censored Survival Outcomes." Diss., Temple University Libraries, 2017. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/466860.

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Statistics<br>Ph.D.<br>One of the major goals in large-scale genomic studies is to identify genes with a prognostic impact on time-to-event outcomes, providing insight into the disease's process. With the rapid developments in high-throughput genomic technologies in the past two decades, the scientific community is able to monitor the expression levels of thousands of genes and proteins resulting in enormous data sets where the number of genomic variables (covariates) is far greater than the number of subjects. It is also typical for such data sets to have a high proportion of censored observa
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25

Zhao, Jianhua, and 赵建华. "Some topics in dimension reduction and clustering." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B4284180X.

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26

Fize, Jacques. "Mise en correspondance de données textuelles hétérogènes fondée sur la dimension spatiale." Thesis, Montpellier, 2019. http://www.theses.fr/2019MONTS099.

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Avec l’essor du Big Data, le traitement du Volume, de la Vélocité (croissance et évolution) et de la Variété de la donnée concentre les efforts des différentes communautés pour exploiter ces nouvelles ressources. Ces nouvelles ressources sont devenues si importantes, que celles-ci sont considérées comme le nouvel « or noir ». Au cours des dernières années, le volume et la vélocité sont des aspects de la donnée qui sont maitrisés contrairement à la variété qui elle reste un défi majeur. Cette thèse présente deux contributions dans le domaine de mise en correspondance de données hétérogènes, ave
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27

Liu, Ye. "Numerical algorithms for data clustering." HKBU Institutional Repository, 2019. https://repository.hkbu.edu.hk/etd_oa/701.

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Data clustering is a process of grouping unlabeled objects based on the imformation describing their relationship. And it has obtained a lot of attentions in data mining for its wide applications in life. For example, in marketing, companys are interested in finding groups of customers with similar purchase behavior, which will help them to make suitable plans to gain more profits. Besides, in biology, we can make use of data clustering to distinguish planets and animals given their features. Whats more, in earthquake analysis, by clustering observed earthquake epicenters, dangerous area can be
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28

Zeng, Yue, and Yue Zeng. "Variable Screening Methods in Multi-Category Problems for Ultra-High Dimensional Data." Diss., The University of Arizona, 2017. http://hdl.handle.net/10150/624579.

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Variable screening techniques are fast and crude techniques to scan high-dimensional data and conduct dimension reduction before a refined variable selection method is applied. Its marginal analysis feature makes the method computationally feasible for ultra-high dimensional problems. However, most existing screening methods for classification problems are designed only for binary classification problems. There is lack of a comprehensive study on variable screening for multi-class classification problems. This research aims to fill the gap by developing variable screening for multi-class probl
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29

Yang, Jing. "Visual hierarchical dimension reduction." Link to electronic thesis, 2002. http://www.wpi.edu/Pubs/ETD/Available/etd-0109102-132821.

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Thesis (M.S.)--Worcester Polytechnic Institute.<br>Keywords: hierarchy; sunburst; dimension reduction; high dimensional data set; multidimensional visualization; parallel coordinates; scatterplot matrices; star glyphs. Includes bibliographical references (p. 86-91).
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30

Kang, Lei. "Reduced-Dimension Hierarchical Statistical Models for Spatial and Spatio-Temporal Data." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1259168805.

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31

Huck, Kevin A. 1972. "Knowledge support for parallel performance data mining." Thesis, University of Oregon, 2009. http://hdl.handle.net/1794/10087.

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xvi, 231 p. : ill. A print copy of this thesis is available through the UO Libraries. Search the library catalog for the location and call number.<br>Parallel applications running on high-end computer systems manifest a complex combination of performance phenomena, such as communication patterns, work distributions, and computational inefficiencies. Current performance tools compute results that help to describe performance behavior, as well as to understand performance problems and how they came about. Unfortunately, parallel performance tool research has been limited in its contributions
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32

Zhou, Xuan. "An Efficient Algorithm for Clustering Genomic Data." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1418910389.

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33

McClurg, Josiah. "Fast demand response with datacenter loads: a green dimension of big data." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5811.

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Demand response is one of the critical technologies necessary for allowing large-scale penetration of intermittent renewable energy sources in the electric grid. Data centers are especially attractive candidates for providing flexible, real-time demand response services to the grid because they are capable of fast power ramp-rates, large dynamic range, and finely-controllable power consumption. This thesis makes a contribution toward implementing load shaping with server clusters through a detailed experimental investigation of three broadly-applicable datacenter workload scenarios. We experim
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34

Lee, Ho-Jin. "Functional data analysis: classification and regression." Texas A&M University, 2004. http://hdl.handle.net/1969.1/2805.

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Functional data refer to data which consist of observed functions or curves evaluated at a finite subset of some interval. In this dissertation, we discuss statistical analysis, especially classification and regression when data are available in function forms. Due to the nature of functional data, one considers function spaces in presenting such type of data, and each functional observation is viewed as a realization generated by a random mechanism in the spaces. The classification procedure in this dissertation is based on dimension reduction techniques of the spaces. One commonly used metho
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35

Mugnier, Martin. "Essays in Panel Data Econometrics." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAG008.

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Cette thèse comporte cinq chapitres portant sur l'étude de quelques problèmes d'identification, d'estimation et d'inférence au sein de modèles semi-paramétriques pour l'analyse économétrique des données de panel. Les quatre premiers chapitres se concentrent sur une classe de modèles dits « à effets fixes », où l'hétérogénéité inobservée par l'économètre est approximée par des variables latentes de faible dimension (relativement à la taille des données) dont la distribution conditionnellement aux variables exogènes n'est pas restreinte. Dans le premier chapitre, nous généralisons un résultat de
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36

Han, Chao. "Bayesian Visual Analytics: Interactive Visualization for High Dimensional Data." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/19210.

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In light of advancements made in data collection techniques over the past two decades, data mining has become common practice to summarize large, high dimensional datasets, in hopes of discovering noteworthy data structures. However, one concern is that most data mining approaches rely upon strict criteria that may mask information in data that analysts may find useful. We propose a new approach called Bayesian Visual Analytics (BaVA) which merges Bayesian Statistics with Visual Analytics to address this concern. The BaVA framework enables experts to interact with the data and the feature disc
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37

Nguyen, David P. "Classification of multisite electrode recordings via variable dimension Gaussian mixtures." Thesis, Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/13929.

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38

Valla, Romain. "Contributions to explainable anomaly detection using data depth." Electronic Thesis or Diss., Institut polytechnique de Paris, 2025. http://www.theses.fr/2025IPPAT020.

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Les événements anormaux sont sujets d'intérêt dans divers domaines d'applications tels que l'industrie, la finance ou encore la médecine dès lors qu'ils permettent de dégager une information qui diffère de la tendance générale. Nous pouvons constater que l'arrivée de nouveaux capteurs interconnectés, plus performants et en plus grand nombre, provoque un accroissement de la masse de données disponible pour des analyses demandant des méthodes innovantes pour résoudre des défis modernes. D'une part le volume croissant des échantillons relevés requiert des algorithmes plus rapides tandis qu'une po
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Choo, Jae gul. "Integration of computational methods and visual analytics for large-scale high-dimensional data." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49121.

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With the increasing amount of collected data, large-scale high-dimensional data analysis is becoming essential in many areas. These data can be analyzed either by using fully computational methods or by leveraging human capabilities via interactive visualization. However, each method has its drawbacks. While a fully computational method can deal with large amounts of data, it lacks depth in its understanding of the data, which is critical to the analysis. With the interactive visualization method, the user can give a deeper insight on the data but suffers when large amounts of data need to be
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Li, Qiongzhu. "Study of Single and Ensemble Machine Learning Models on Credit Data to Detect Underlying Non-performing Loans." Thesis, Uppsala universitet, Statistiska institutionen, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-297080.

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In this paper, we try to compare the performance of two feature dimension reduction methods, the LASSO and PCA. Both simulation study and empirical study show that the LASSO is superior to PCA when selecting significant variables. We apply Logistics Regression (LR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT) and their corresponding ensemble machines constructed by bagging and adaptive boosting (adaboost) in our study. Three experiments are conducted to explore the impact of class-unbalanced data set on all models. Empirical study indicates that when the p
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Zuur, Alain Francois. "Dimension reduction techniques in community ecology : with applications to spatio-temporal marine ecological data." Thesis, University of Aberdeen, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287716.

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The aim of this PhD-thesis is to develop techniques which can be used to analyse spatio-temporal ecological data sets. Central questions are: A. What are the relationships between species abundances and spatial environmental variables in a particular year? What are relationships between species? B. How do these species-environmental relations and species interactions change from year-to-year? What is the effect of global environmental variables on these year-to-year variations? The thesis is divided into two parts. In Part I, we concentrate on the first question. We discuss the state-of-the-ar
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Singhal, Kritika. "Geometric Methods for Simplification and Comparison of Data Sets." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587253879303425.

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Self, Jessica Zeitz. "Designing and Evaluating Object-Level Interaction to Support Human-Model Communication in Data Analysis." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/70950.

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High-dimensional data appear in all domains and it is challenging to explore. As the number of dimensions in datasets increases, the harder it becomes to discover patterns and develop insights. Data analysis and exploration is an important skill given the amount of data collection in every field of work. However, learning this skill without an understanding of high-dimensional data is challenging. Users naturally tend to characterize data in simplistic one-dimensional terms using metrics such as mean, median, mode. Real-world data is more complex. To gain the most insight from data, users nee
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Chayka, Oleksiy. "Three Case Studies For Understanding, Measuring and Using a Compound Notion of Data Quality With Emphasis on the data Staleness Dimension." Doctoral thesis, Università degli studi di Trento, 2012. https://hdl.handle.net/11572/368390.

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By its nature, the term “data quality†with its generic meaning “fitness for use†has both subjective and objective aspects. There are numerous methodologies and techniques to evaluate its subjective parts and to measure its objective parts. However, none of them are uniform enough for exploitation in diverse real-world applications. None of those, in fact, can be created as such, since data quality penetrates too deep into business operations to prevent from finding “a silver bullet†for all of them: it normally goes from representation of real world entities or their properties w
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Chayka, Oleksiy. "Three Case Studies For Understanding, Measuring and Using a Compound Notion of Data Quality With Emphasis on the data Staleness Dimension." Doctoral thesis, University of Trento, 2012. http://eprints-phd.biblio.unitn.it/872/1/PhD_Thesis.pdf.

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By its nature, the term “data quality” with its generic meaning “fitness for use” has both subjective and objective aspects. There are numerous methodologies and techniques to evaluate its subjective parts and to measure its objective parts. However, none of them are uniform enough for exploitation in diverse real-world applications. None of those, in fact, can be created as such, since data quality penetrates too deep into business operations to prevent from finding “a silver bullet” for all of them: it normally goes from representation of real world entities or their properties with data in
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Chulyadyo, Rajani. "Un nouvel horizon pour la recommandation : intégration de la dimension spatiale dans l'aide à la décision." Thesis, Nantes, 2016. http://www.theses.fr/2016NANT4012/document.

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De nos jours, il est très fréquent de représenter un système en termes de relations entre objets. Parmi les applications les plus courantes de telles données relationnelles, se situent les systèmes de recommandation (RS), qui traitent généralement des relations entre utilisateurs et items à recommander. Les modèles relationnels probabilistes (PRM) sont un bon choix pour la modélisation des dépendances probabilistes entre ces objets. Une tendance croissante dans les systèmes de recommandation est de rajouter une dimension spatiale à ces objets, que ce soient les utilisateurs, ou les items. Cett
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47

"Digital photo album management techniques: from one dimension to multi-dimension." 2005. http://library.cuhk.edu.hk/record=b5892580.

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Lu Yang.<br>Thesis submitted in: November 2004.<br>Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.<br>Includes bibliographical references (leaves 96-103).<br>Abstracts in English and Chinese.<br>Abstract --- p.i<br>Acknowledgement --- p.iv<br>Chapter 1 --- Introduction --- p.1<br>Chapter 1.1 --- Motivation --- p.1<br>Chapter 1.2 --- Our Contributions --- p.3<br>Chapter 1.3 --- Thesis Outline --- p.5<br>Chapter 2 --- Background Study --- p.7<br>Chapter 2.1 --- MPEG-7 Introduction --- p.8<br>Chapter 2.2 --- Image Analysis in CBIR Systems --- p.11<br>Chapter 2.2.1 --- Color Info
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48

Niu, Po-Yao, and 牛柏堯. "Dimension Reduction for Tensor Structure Data." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/85341886950959693481.

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碩士<br>國立臺灣大學<br>數學研究所<br>102<br>The advances of technologies have created a new era for data collections that the data size and its complexity becomes very challenging to data analysts. Dimension reduction is a key process for statistical inference when facing huge data set. Principal component analysis (PCA) may be the most popular dimension reduction method for vector data. PCA projects the data to a lower space and the features become uncorrelated in the new space, but, in reality, it could be inefficient due to small sample size and large feature dimension. Multilinear principal component
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CHANG, FANG-CHI, and 張芳綺. "A Comparison of Dimension Reduction Methods for High-dimensional Sparse Data with Application to Text Data." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/cu9qdp.

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碩士<br>國立臺北大學<br>統計學系<br>106<br>The term frequency is an important quantity in the analysis of text data. It represents the frequency of some specified terms that occurs in the text documents. Therefore, the creation of the structured term frequency matrix from a large number of unstructured text documents is the first step in the process of text mining. More commonly the term frequency matrix is a high-dimensional sparse matrix. However, the result of analysis might be misled if this matrix consists of a large rare terms and/or redundant terms. In addition, the complexity of the analysis would
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Silva, José Miguel Parreira e. "Finding the Critical Feature Dimension of Big Datasets." Master's thesis, 2017. http://hdl.handle.net/10316/82847.

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Dissertação de Mestrado em Engenharia Informática apresentada à Faculdade de Ciências e Tecnologia<br>Big Data allied to the Internet of Things nowadays provides a powerful resource that various organizations are increasingly exploiting for applications ranging from decision support, predictive and prescriptive analytics, to knowledge and intelligence discovery. In analytics and data mining processes, it is usually desirable to have as much data as possible, though it is often more important that the data is of high quality thereby raising two of the most important problems when handling larg
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