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Books on the topic 'Data dimension'

Author: Grafiati

Published: 28 May 2022

Last updated: 30 July 2025

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1

Gorban, Alexander N., Balázs Kégl, Donald C. Wunsch, and Andrei Y. Zinovyev, eds. Principal Manifolds for Data Visualization and Dimension Reduction. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-73750-6.

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2

N, Gorbanʹ A., ed. Principal manifolds for data visualization and dimension reduction. Springer, 2007.

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3

Perlman, Geoff. Inside 4th Dimension 3.1. 2nd ed. SYBEX, 1994.

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4

Sindler, Hal. MRDS - 4D: Mineral Resources Data System using 4th DIMENSION. U.S. Dept. of the Interior, U.S. Geological Survey, 1995.

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5

Sindler, Hal. MRDS - 4D: Mineral Resources Data System using 4th DIMENSION. U.S. Dept. of the Interior, U.S. Geological Survey, 1995.

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6

Sindler, Hal. MRDS - 4D: Mineral Resources Data System using 4th DIMENSION. U.S. Dept. of the Interior, U.S. Geological Survey, 1995.

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7

Zhu, Lei, Jingjing Li, and Zheng Zhang. Dynamic Graph Learning for Dimension Reduction and Data Clustering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-42313-0.

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8

F, Nardocchio Elaine, ed. Reader response to literature: The empirical dimension. Mouton de Gruyter, 1992.

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9

Schimek, Heimo, and Charles Jencks. Cybertecture: Die 4. Dimension in der Architektur. Löcker Verlag, 2001.

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10

Mitchell, Jeremy. Electronic banking and the consumer: The European dimension. Policy Studies Institute, 1988.

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11

Mitchell, Jeremy. Electronic banking and the consumer: The European dimension. Policy Studies Institute, 1988.

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12

John, Beckmann, ed. The virtual dimension: Architecture, representation, and crash culture. Princeton Architectural Press, 1998.

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13

Ebel, Karl-H. Computer-integrated manufacturing: The social dimension. International Labour Office, 1990.

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14

David, Wheatley, and Leonardo García Sanjuán. Mapping the future of the past: Managing the spatial dimension of the European archaeological resource. Edited by Instituto Andaluz del Patrimonio Histórico. Universidad de Sevilla, 2002.

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15

Evans, James W. Computer programs for adjusting the mechanical properties of 2-inch dimension lumber for changes in moisture content. U.S. Dept. of Agriculture, Forest Service, Forest Products Laboratory, 1990.

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16

Simha, Aditya. National Culture Concepts and Dimension Scores: Using Data to Determine a Religious Cult’s Next Base of Operations. Edited by Justin O’Brien and Thomas Wainwright. SAGE Publications, Inc., 2024. http://dx.doi.org/10.4135/9781071971468.

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17

C, Foster Stephen, ed. Dada/dimensions. UMI Research Press, 1985.

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18

C, Foster Stephen, ed. Dada / dimensions. UMI Research Press, 1985.

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19

Masulli, Francesco, Alfredo Petrosino, and Stefano Rovetta, eds. Clustering High--Dimensional Data. Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48577-4.

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20

Shinmura, Shuichi. High-dimensional Microarray Data Analysis. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5998-9.

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21

Bühlmann, Peter, and Sara van de Geer. Statistics for High-Dimensional Data. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20192-9.

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22

Jacoby, William. Data Theory and Dimensional Analysis. SAGE Publications, Inc., 1991. http://dx.doi.org/10.4135/9781412983860.

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23

Bolón-Canedo, Verónica, Noelia Sánchez-Maroño, and Amparo Alonso-Betanzos. Feature Selection for High-Dimensional Data. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21858-8.

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24

Frigessi, Arnoldo, Peter Bühlmann, Ingrid K. Glad, Mette Langaas, Sylvia Richardson, and Marina Vannucci, eds. Statistical Analysis for High-Dimensional Data. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27099-9.

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25

Brown, Alistair R. Interpretation of three-dimensional seismic data. American Association of Petroleum Geologists, 1986.

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26

Brown, Alistair R. Interpretation of three-dimensional seismic data. 2nd ed. American Association of Petroleum Geologists, 1988.

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27

Brown, Alistair R. Interpretation of three-dimensional seismic data. 3rd ed. American Association of Petroleum Geologists, 1991.

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28

1936-, Kent William, and Hewlett-Packard Laboratories, eds. Measurement data (archive report). Hewlett-Packard Laboratories, Technical Publications Department, 1996.

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29

Data quality: The accuracy dimension. Morgan Kaufmann, 2003.

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30

Olson, Jack E. Data Quality: The Accuracy Dimension. Elsevier Science & Technology Books, 2003.

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31

Data-Parallel Computing: The Language Dimension. Itp New Media, 1996.

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32

Alexander, N. Gorban Donald C. II Wunsch Bal Zs K. Gl. Principal Manifolds for Data Visualization and Dimension Reduction. Springer, 2008.

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33

Principal Manifolds for Data Visualization and Dimension Reduction. Springer London, Limited, 2007.

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34

Olson, Jack E. Data Quality: The Accuracy Dimension (The Morgan Kaufmann Series in Data Management Systems). Morgan Kaufmann, 2002.

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35

Greco, Luca, and Alessio Farcomeni. Robust Methods for Data Reduction. Taylor & Francis Group, 2015.

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36

Robust Methods for Data Reduction. Taylor & Francis Group, 2016.

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37

Dynamic Graph Learning for Dimension Reduction and Data Clustering. Springer, 2023.

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38

Yang, Jiao. Statistical Methods For Estimating The Dimension Of Multivariate Data. Proquest, Umi Dissertation Publishing, 2011.

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39

Ailbhe Walsh, Client Sales Support Executive at Dimension Data. Blurb, 2016.

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40

Geometric Structure of HighDimensional Data and Dimensionality Reduction. Springer, 2012.

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41

National Aeronautics and Space Administration (NASA) Staff. Neural Network Machine Learning and Dimension Reduction for Data Visualization. Independently Published, 2019.

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42

Muchnick, V. S., and A. V. Shafarenko. Data Parallel Computing: The Language Dimension (Parallel and Distributed Computing). Chapman & Hall, 1995.

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43

Nonparametric studies of doubly stochastic Poisson processes, binomial data, and high dimension, low sample size data. 2008.

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44

Zhang, Wen'gang. MARS Applications in Geotechnical Engineering Systems: Multi-Dimension with Big Data. Springer Singapore Pte. Limited, 2019.

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45

Development Dimension Private Philanthropy for Development - Second Edition Data for Action. Organization for Economic Cooperation & Development, 2021.

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46

Virtual Medicine: A New Dimension in Energy Healing: Discover Where Ancient Wisdom Meets Breakthrough Technology. Thorsons, 1999.

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47

Vanrolleghem, Peter A. Integrated Assessment for Water Framework Directive Implementation: Data, Economic and Human Dimension. IWA Publishing, 2010.

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48

Hall, Peter. Principal component analysis for functional data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.8.

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This article discusses the methodology and theory of principal component analysis (PCA) for functional data. It first provides an overview of PCA in the context of finite-dimensional data and infinite-dimensional data, focusing on functional linear regression, before considering the applications of PCA for functional data analysis, principally in cases of dimension reduction. It then describes adaptive methods for prediction and weighted least squares in functional linear regression. It also examines the role of principal components in the assessment of density for functional data, showing how

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49

Ruspini, Elisabetta, and Angela Dale, eds. The Gender Dimension of Social Change. Bristol University Press, 2002. http://dx.doi.org/10.46692/9781847425478.

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This new study uses longitudinal data to provide new insights into the changing dynamics of lives of women today. In particular, it explores the potential of longitudinal or life course analysis as a powerful tool for appreciating the gender dimension of social life.

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50

Ltd, ICON Group, and ICON Group International Inc. DIMENSION DATA HOLDINGS LTD: Labor Productivity Benchmarks and International Gap Analysis (Labor Productivity Series). 2nd ed. Icon Group International, 2000.

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