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Advances in Principal Component Analysis / / edited by Fausto Pedro García Márquez
Advances in Principal Component Analysis / / edited by Fausto Pedro García Márquez
Pubbl/distr/stampa London, United Kingdom : , : IntechOpen, , 2022
Descrizione fisica 1 online resource (252 pages) : illustrations
Disciplina 519.5354
Soggetto topico Principal components analysis
ISBN 1-80355-766-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. The Foundation for Open Component Analysis: A System of Systems Hyper Framework Model -- 2. Identification of Multilinear Systems: A Brief Overview -- 3. Evaluation of Principal Component Analysis Variants to Assess Their Suitability for Mobile Malware Detection -- 4. Principal Component Analysis and Artificial Intelligence Approaches for Solar Photovoltaic Power Forecasting -- 5. Variable Selection in Nonlinear Principal Component Analysis -- 6. Space-Time-Parameter PCA for Data-Driven Modeling with Application to Bioengineering -- 7. Principal Component Analysis in Financial Data Science -- 8. Determining an Adequate Number of Principal Components -- 9. Spatial Principal Component Analysis of Head-Related Transfer Functions and Its Domain Dependency -- 10. Prediction Analysis Based on Logistic Regression Modelling -- 11. On the Use of Modified Winsorization with Graphical Diagnostic for Obtaining a Statistically Optimal Classification Accuracy in Predictive Discriminant Analysis -- 12. Mode Interpretation of Aerodynamic Characteristics of Tall Buildings Subject to Twisted Winds.
Record Nr. UNINA-9910633975203321
London, United Kingdom : , : IntechOpen, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advances in principal component analysis / / edited by Fausto Pedro García Márquez
Advances in principal component analysis / / edited by Fausto Pedro García Márquez
Pubbl/distr/stampa London, England : , : IntechOpen, , 2022
Descrizione fisica 1 online resource (252 pages)
Disciplina 519.5354
Soggetto topico Principal components analysis
Correspondence analysis (Statistics)
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. The Foundation for Open Component Analysis: A System of Systems Hyper Framework Model -- 2. Identification of Multilinear Systems: A Brief Overview -- 3. Evaluation of Principal Component Analysis Variants to Assess Their Suitability for Mobile Malware Detection -- 4. Principal Component Analysis and Artificial Intelligence Approaches for Solar Photovoltaic Power Forecasting -- 5. Variable Selection in Nonlinear Principal Component Analysis -- 6. Space-Time-Parameter PCA for Data-Driven Modeling with Application to Bioengineering -- 7. Principal Component Analysis in Financial Data Science -- 8. Determining an Adequate Number of Principal Components -- 9. Spatial Principal Component Analysis of Head-Related Transfer Functions and Its Domain Dependency -- 10. Prediction Analysis Based on Logistic Regression Modelling -- 11. On the Use of Modified Winsorization with Graphical Diagnostic for Obtaining a Statistically Optimal Classification Accuracy in Predictive Discriminant Analysis -- 12. Mode Interpretation of Aerodynamic Characteristics of Tall Buildings Subject to Twisted Winds.
Record Nr. UNINA-9910688391503321
London, England : , : IntechOpen, , 2022
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied multiway data analysis [[electronic resource] /] / Pieter M. Kroonenberg
Applied multiway data analysis [[electronic resource] /] / Pieter M. Kroonenberg
Autore Kroonenberg Pieter M
Pubbl/distr/stampa Hoboken, N.J., : Wiley-Interscience, c2008
Descrizione fisica 1 online resource (614 p.)
Disciplina 519.5
519.5/35
519.535
Collana Wiley series in probability and statistics
Soggetto topico Multivariate analysis
Multiple comparisons (Statistics)
Principal components analysis
Soggetto genere / forma Electronic books.
ISBN 1-281-23734-5
9786611237349
0-470-23800-3
0-470-23799-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto APPLIED MULTIWAY DATA ANALYSIS; CONTENTS; Foreword; Preface; PART I DATA, MODELS, AND ALGORITHMS; 1 Overture; 1.1 Three-way and multiway data; 1.2 Multiway data analysis; 1.3 Before the arrival of three-mode analysis; 1.4 Three-mode data-analytic techniques; 1.5 Example: Judging Chopin's preludes; 1.6 Birth of the Tucker model; 1.7 Current status of multiway analysis; 2 Overview; 2.1 What are multiway data?; 2.2 Why multiway analysis?; 2.3 What is a model?; 2.4 Some history; 2.5 Multiway models and methods; 2.6 Conclusions; 3 Three-Way and Multiway Data; 3.1 Chapter preview; 3.2 Terminology
3.3 Two-way solutions to three-way data3.4 Classification principles; 3.5 Overview of three-way data designs; 3.6 Fully crossed designs; 3.7 Nested designs; 3.8 Scaling designs; 3.9 Categorical data; 4 Component Models for Fully-Crossed Designs; 4.1 Introduction; 4.2 Chapter preview; 4.3 Two-mode modeling of three-way data; 4.4 Extending two-mode component models to three-mode models; 4.5 Tucker models; 4.6 Parafac models; 4.7 ParaTuck2 model; 4.8 Core arrays; 4.9 Relationships between component models; 4.10 Multiway component modeling under constraints; 4.11 Conclusions
5 Algorithms for Multiway Models5.1 Introduction; 5.2 Chapter preview; 5.3 Terminology and general issues; 5.4 An example of an iterative algorithm; 5.5 General behavior of multiway algorithms; 5.6 The Parallel factor model - Parafac; 5.7 The Tucker models; 5.8 STATIS; 5.9 Conclusions; PART II DATA HANDLING, MODEL SELECTION, AND INTERPRETATION; 6 Preprocessing; 6.1 Introduction; 6.2 Chapter preview; 6.3 General considerations; 6.4 Model-based arguments for preprocessing choices; 6.5 Content-based arguments for preprocessing choices; 6.6 Preprocessing and specific multiway data designs
6.7 Centering and analysis-of-variance models: Two-way data6.8 Centering and analysis-of-variance models: Three-way data; 6.9 Recommendations; 7 Missing Data in Multiway Analysis; 7.1 Introduction; 7.2 Chapter preview; 7.3 Handling missing data in two-mode PCA; 7.4 Handling missing data in multiway analysis; 7.5 Multiple imputation in multiway analysis: Data matters; 7.6 Missing data in multiway analysis: Practice; 7.7 Example: Spanjer's Chromatography data; 7.8 Example: NICHD Child care data; 7.9 Further applications; 7.10 Computer programs for multiple imputation
8 Model and Dimensionality Selection8.1 Introduction; 8.2 Chapter preview; 8.3 Sample size and stochastics; 8.4 Degrees of freedom; 8.5 Selecting the dimensionality of a Tucker model; 8.6 Selecting the dimensionality of a Parafac model; 8.7 Model selection from a hierarchy; 8.8 Model stability and predictive power; 8.9 Example: Chopin prelude data; 8.10 Conclusions; 9 Interpreting Component Models; 9.1 Chapter preview; 9.2 General principles; 9.3 Representations of component models; 9.4 Scaling of components; 9.5 Interpreting core arrays; 9.6 Interpreting extended core arrays
9.7 Special topics
Record Nr. UNINA-9910145562403321
Kroonenberg Pieter M  
Hoboken, N.J., : Wiley-Interscience, c2008
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Applied multiway data analysis / / Pieter M. Kroonenberg
Applied multiway data analysis / / Pieter M. Kroonenberg
Autore Kroonenberg Pieter M
Pubbl/distr/stampa Hoboken, N.J., : Wiley-Interscience, c2008
Descrizione fisica 1 online resource (614 p.)
Disciplina 519.5/35
Collana Wiley series in probability and statistics
Soggetto topico Multivariate analysis
Multiple comparisons (Statistics)
Principal components analysis
ISBN 1-281-23734-5
9786611237349
0-470-23800-3
0-470-23799-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto APPLIED MULTIWAY DATA ANALYSIS; CONTENTS; Foreword; Preface; PART I DATA, MODELS, AND ALGORITHMS; 1 Overture; 1.1 Three-way and multiway data; 1.2 Multiway data analysis; 1.3 Before the arrival of three-mode analysis; 1.4 Three-mode data-analytic techniques; 1.5 Example: Judging Chopin's preludes; 1.6 Birth of the Tucker model; 1.7 Current status of multiway analysis; 2 Overview; 2.1 What are multiway data?; 2.2 Why multiway analysis?; 2.3 What is a model?; 2.4 Some history; 2.5 Multiway models and methods; 2.6 Conclusions; 3 Three-Way and Multiway Data; 3.1 Chapter preview; 3.2 Terminology
3.3 Two-way solutions to three-way data3.4 Classification principles; 3.5 Overview of three-way data designs; 3.6 Fully crossed designs; 3.7 Nested designs; 3.8 Scaling designs; 3.9 Categorical data; 4 Component Models for Fully-Crossed Designs; 4.1 Introduction; 4.2 Chapter preview; 4.3 Two-mode modeling of three-way data; 4.4 Extending two-mode component models to three-mode models; 4.5 Tucker models; 4.6 Parafac models; 4.7 ParaTuck2 model; 4.8 Core arrays; 4.9 Relationships between component models; 4.10 Multiway component modeling under constraints; 4.11 Conclusions
5 Algorithms for Multiway Models5.1 Introduction; 5.2 Chapter preview; 5.3 Terminology and general issues; 5.4 An example of an iterative algorithm; 5.5 General behavior of multiway algorithms; 5.6 The Parallel factor model - Parafac; 5.7 The Tucker models; 5.8 STATIS; 5.9 Conclusions; PART II DATA HANDLING, MODEL SELECTION, AND INTERPRETATION; 6 Preprocessing; 6.1 Introduction; 6.2 Chapter preview; 6.3 General considerations; 6.4 Model-based arguments for preprocessing choices; 6.5 Content-based arguments for preprocessing choices; 6.6 Preprocessing and specific multiway data designs
6.7 Centering and analysis-of-variance models: Two-way data6.8 Centering and analysis-of-variance models: Three-way data; 6.9 Recommendations; 7 Missing Data in Multiway Analysis; 7.1 Introduction; 7.2 Chapter preview; 7.3 Handling missing data in two-mode PCA; 7.4 Handling missing data in multiway analysis; 7.5 Multiple imputation in multiway analysis: Data matters; 7.6 Missing data in multiway analysis: Practice; 7.7 Example: Spanjer's Chromatography data; 7.8 Example: NICHD Child care data; 7.9 Further applications; 7.10 Computer programs for multiple imputation
8 Model and Dimensionality Selection8.1 Introduction; 8.2 Chapter preview; 8.3 Sample size and stochastics; 8.4 Degrees of freedom; 8.5 Selecting the dimensionality of a Tucker model; 8.6 Selecting the dimensionality of a Parafac model; 8.7 Model selection from a hierarchy; 8.8 Model stability and predictive power; 8.9 Example: Chopin prelude data; 8.10 Conclusions; 9 Interpreting Component Models; 9.1 Chapter preview; 9.2 General principles; 9.3 Representations of component models; 9.4 Scaling of components; 9.5 Interpreting core arrays; 9.6 Interpreting extended core arrays
9.7 Special topics
Record Nr. UNINA-9910876930003321
Kroonenberg Pieter M  
Hoboken, N.J., : Wiley-Interscience, c2008
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Comparison tools for assessing the microgravity environment of missions, carriers and conditions / / Richard DeLombard [and three others]
Comparison tools for assessing the microgravity environment of missions, carriers and conditions / / Richard DeLombard [and three others]
Autore DeLombard Richard
Pubbl/distr/stampa Cleveland, Ohio : , : National Aeronautics and Space Administration, Lewis Research Center, , April 1997
Descrizione fisica 1 online resource (vii, 53 pages) : illustrations
Collana NASA TM
Soggetto topico Microgravity
Acceleration measurement
Principal components analysis
Spectrum analysis
Spacecraft environments
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910707608903321
DeLombard Richard  
Cleveland, Ohio : , : National Aeronautics and Space Administration, Lewis Research Center, , April 1997
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Constrained principal component analysis and related techniques / / Yoshio Takane, Professor Emeritus, McGill University Montreal, Quebec, Canada and Adjunct Professor at University of Victoria British Columbia, Canada
Constrained principal component analysis and related techniques / / Yoshio Takane, Professor Emeritus, McGill University Montreal, Quebec, Canada and Adjunct Professor at University of Victoria British Columbia, Canada
Autore Takane Yoshio
Edizione [1st edition]
Pubbl/distr/stampa Boca Raton : , : Chapman and Hall/CRC, , 2014
Descrizione fisica 1 online resource (244 p.)
Disciplina 519.5/35
Collana Monographs on statistics and applied probability
Soggetto topico Principal components analysis
Multivariate analysis
ISBN 0-429-18837-4
1-4665-5666-8
Classificazione MAT029000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front Cover; Contents; List of Figures; List of Tables; Preface; About the Author; Chapter 1 Introduction; Chapter 2 Mathematical Foundation; Chapter 3 Constrained Principal Component Analysis (CPCA); Chapter 4 Special Cases and Related Methods; Chapter 5 Related Topics of Interest; Chapter 6 Different Constraints on Different Dimensions (DCDD); Epilogue; Appendix; Bibliography; Back Cover
Record Nr. UNINA-9910787586203321
Takane Yoshio  
Boca Raton : , : Chapman and Hall/CRC, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Constrained principal component analysis and related techniques / / Yoshio Takane, Professor Emeritus, McGill University Montreal, Quebec, Canada and Adjunct Professor at University of Victoria British Columbia, Canada
Constrained principal component analysis and related techniques / / Yoshio Takane, Professor Emeritus, McGill University Montreal, Quebec, Canada and Adjunct Professor at University of Victoria British Columbia, Canada
Autore Takane Yoshio
Edizione [1st edition]
Pubbl/distr/stampa Boca Raton : , : Chapman and Hall/CRC, , 2014
Descrizione fisica 1 online resource (244 p.)
Disciplina 519.5/35
Collana Monographs on statistics and applied probability
Soggetto topico Principal components analysis
Multivariate analysis
ISBN 0-429-18837-4
1-4665-5666-8
Classificazione MAT029000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Front Cover; Contents; List of Figures; List of Tables; Preface; About the Author; Chapter 1 Introduction; Chapter 2 Mathematical Foundation; Chapter 3 Constrained Principal Component Analysis (CPCA); Chapter 4 Special Cases and Related Methods; Chapter 5 Related Topics of Interest; Chapter 6 Different Constraints on Different Dimensions (DCDD); Epilogue; Appendix; Bibliography; Back Cover
Record Nr. UNINA-9910799902203321
Takane Yoshio  
Boca Raton : , : Chapman and Hall/CRC, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Discrimination of objects within polarimetric imagery using principle component and cluster analysis [[electronic resource] /] / Melvin Felton ... [and others]
Discrimination of objects within polarimetric imagery using principle component and cluster analysis [[electronic resource] /] / Melvin Felton ... [and others]
Pubbl/distr/stampa Adelphi, MD : , : Army Research Laboratory, , [2007]
Descrizione fisica 1 online resource (vi, 22 pages) : illustrations (some color)
Altri autori (Persone) FeltonMelvin A
Collana ARL-TR
Soggetto topico Polarimetry
Infrared imaging
Cluster analysis
Principal components analysis
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910701472803321
Adelphi, MD : , : Army Research Laboratory, , [2007]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Independent component analysis / / Aapo Hyvarinen, Juha Karhunen, Erkki Oja
Independent component analysis / / Aapo Hyvarinen, Juha Karhunen, Erkki Oja
Autore Hyvarinen Aapo
Pubbl/distr/stampa New York, : J. Wiley, c2001
Descrizione fisica 1 online resource (505 p.)
Disciplina 519.5/35
Altri autori (Persone) KarhunenJuha
OjaErkki
Collana Adaptive and learning systems for signal processing, communications, and control
Soggetto topico Multivariate analysis
Principal components analysis
ISBN 1-280-26480-2
9786610264803
0-470-30861-3
0-471-46419-8
0-471-22131-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Contents; Preface; 1 Introduction; 1.1 Linear representation of multivariate data; 1.1.1 The general statistical setting; 1.1.2 Dimension reduction methods; 1.1.3 Independence as a guiding principle; 1.2 Blind source separation; 1.2.1 Observing mixtures of unknown signals; 1.2.2 Source separation based on independence; 1.3 Independent component analysis; 1.3.1 Definition; 1.3.2 Applications; 1.3.3 How to find the independent components; 1.4 History of ICA; Part I: MATHEMATICAL PRELIMINARIES; 2 Random Vectors and Independence; 2.1 Probability distributions and densities
2.2 Expectations and moments2.3 Uncorrelatedness and independence; 2.4 Conditional densities and Bayes' rule; 2.5 The multivariate gaussian density; 2.6 Density of a transformation; 2.7 Higher-order statistics; 2.8 Stochastic processes *; 2.9 Concluding remarks and references; Problems; 3 Gradients and Optimization Methods; 3.1 Vector and matrix gradients; 3.2 Learning rules for unconstrained optimization; 3.3 Learning rules for constrained optimization; 3.4 Concluding remarks and references; Problems; 4 Estimation Theory; 4.1 Basic concepts; 4.2 Properties of estimators
4.3 Method of moments4.4 Least-squares estimation; 4.5 Maximum likelihood method; 4.6 Bayesian estimation *; 4.7 Concluding remarks and references; Problems; 5 Information Theory; 5.1 Entropy; 5.2 Mutual information; 5.3 Maximum entropy; 5.4 Negentropy; 5.5 Approximation of entropy by cumulants; 5.6 Approximation of entropy by nonpolynomial functions; 5.7 Concluding remarks and references; Problems; Appendix proofs; 6 Principal Component Analysis and Whitening; 6.1 Principal components; 6.2 PCA by on-line learning; 6.3 Factor analysis; 6.4 Whitening; 6.5 Orthogonalization
6.6 Concluding remarks and referencesProblems; Part II: BASIC INDEPENDENT COMPONENT ANALYSIS; 7 What is Independent Component Analysis?; 7.1 Motivation; 7.2 Definition of independent component analysis; 7.3 Illustration of ICA; 7.4 ICA is stronger that whitening; 7.5 Why gaussian variables are forbidden; 7.6 Concluding remarks and references; Problems; 8 ICA by Maximization of Nongaussianity; 8.1 ""Nongaussian is independent""; 8.2 Measuring nongaussianity by kurtosis; 8.3 Measuring nongaussianity by negentropy; 8.4 Estimating several independent components; 8.5 ICA and projection pursuit
8.6 Concluding remarks and referencesProblems; Appendix proofs; 9 ICA by Maximum Likelihood Estimation; 9.1 The likelihood of the ICA model; 9.2 Algorithms for maximum likelihood estimation; 9.3 The infomax principle; 9.4 Examples; 9.5 Concluding remarks and references; Problems; Appendix proofs; 10 ICA by Minimization of Mutual Information; 10.1 Defining ICA by mutual information; 10.2 Mutual information and nongaussianity; 10.3 Mutual information and likelihood; 10.4 Algorithms for minimization of mutual information; 10.5 Examples; 10.6 Concluding remarks and references; Problems
11 ICA by Tensorial Methods
Record Nr. UNINA-9910143176003321
Hyvarinen Aapo  
New York, : J. Wiley, c2001
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Independent component analysis / Aapo Hyvärinen, Juha Karhunen, Erkki Oja
Independent component analysis / Aapo Hyvärinen, Juha Karhunen, Erkki Oja
Autore Hyvarinen, Aapo
Pubbl/distr/stampa New York : J. Wiley, c2001
Descrizione fisica xxi, 481 p. : ill. ; 25 cm
Disciplina 519.535
Altri autori (Persone) Karhunen, Juhaauthor
Oja, Erkkiauthor
Collana Adaptive and learning systems for signal processing, communications, and control
Soggetto topico Multivariate analysis
Principal components analysis
ISBN 047140540X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISALENTO-991004128879707536
Hyvarinen, Aapo  
New York : J. Wiley, c2001
Materiale a stampa
Lo trovi qui: Univ. del Salento
Opac: Controlla la disponibilità qui