LEADER 06929nam 2200517 450 001 996483154003316 005 20230718150844.0 010 $a3-031-05371-0 035 $a(MiAaPQ)EBC7054654 035 $a(Au-PeEL)EBL7054654 035 $a(CKB)24294147000041 035 $a(PPN)263902595 035 $a(EXLCZ)9924294147000041 100 $a20230107d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aDimensionality reduction in data science /$fMax Garzon [and five others] 210 1$aCham, Switzerland :$cSpringer,$d[2022] 210 4$d©2022 215 $a1 online resource (268 pages) $cillustrations 311 08$aPrint version: Garzon, Max Dimensionality Reduction in Data Science Cham : Springer International Publishing AG,c2022 9783031053702 320 $aIncludes bibliographical references. 327 $aIntro -- Preface -- Contents -- Acronyms -- 1 What Is Data Science (DS)? -- 1.1 Major Families of Data Science Problems -- 1.1.1 Classification Problems -- 1.1.2 Prediction Problems -- 1.1.3 Clustering Problems -- 1.2 Data, Big Data, and Pre-processing -- 1.2.1 What Is Data? -- 1.2.2 Big Data -- 1.2.3 Data Cleansing -- 1.2.3.1 Duplication -- 1.2.3.2 Fixing/Removing Errors -- 1.2.3.3 Missing Data -- 1.2.3.4 Outliers -- 1.2.3.5 Multicollinearity -- 1.2.4 Data Visualization -- 1.2.5 Data Understanding -- 1.3 Populations and Data Sampling -- 1.3.1 Sampling -- 1.3.2 Training, Testing, and Validation -- 1.4 Overview and Scope -- 1.4.1 Prerequisites and Layout -- 1.4.2 Data Science Methodology -- 1.4.3 Scope of the Book -- Reference -- 2 Solutions to Data Science Problems -- 2.1 Conventional Statistical Solutions -- 2.1.1 Linear Multiple Regression Model: Continuous Response -- 2.1.1.1 Akaike Information Criterion (AIC) -- 2.1.1.2 Bayesian Information Criterion (BIC) -- 2.1.1.3 Adjusted R-Squared -- 2.1.2 Logistic Regression: Categorical Response -- 2.1.3 Variable Selection and Model Building -- 2.1.4 Generalized Linear Model (GLM) -- 2.1.5 Decision Trees -- 2.1.6 Bayesian Learning -- 2.2 Machine Learning Solutions: Supervised -- 2.2.1 k-Nearest Neighbors (kNN) -- 2.2.2 Ensemble Methods -- 2.2.3 Support Vector Machines (SVMs) -- 2.2.4 Neural Networks (NNs) -- 2.3 Machine Learning Solutions: Unsupervised -- 2.3.1 Hard Clustering -- 2.3.2 Soft Clustering -- 2.4 Controls, Evaluation, and Assessment -- 2.4.1 Evaluation Methods -- 2.4.2 Metrics for Assessment -- References -- 3 What Is Dimensionality Reduction (DR)? -- 3.1 Dimensionality Reduction -- 3.2 Major Approaches to Dimensionality Reduction -- 3.2.1 Conventional Statistical Approaches -- 3.2.2 Geometric Approaches -- 3.2.3 Information-Theoretic Approaches -- 3.2.4 Molecular Computing Approaches. 327 $a3.3 The Blessings of Dimensionality -- References -- 4 Conventional Statistical Approaches -- 4.1 Principal Component Analysis (PCA) -- 4.1.1 Obtaining the Principal Components -- 4.1.2 Singular Value Decomposition (SVD) -- 4.2 Nonlinear PCA -- 4.2.1 Kernel PCA -- 4.2.2 Independent Component Analysis (ICA) -- 4.3 Nonnegative Matrix Factorization (NMF) -- 4.3.1 Approximate Solutions -- 4.3.2 Clustering and Other Applications -- 4.4 Discriminant Analysis -- 4.4.1 Linear Discriminant Analysis (LDA) -- 4.4.2 Quadratic Discriminant Analysis (QDA) -- 4.5 Sliced Inverse Regression (SIR) -- References -- 5 Geometric Approaches -- 5.1 Introduction to Manifolds -- 5.2 Manifold Learning Methods -- 5.2.1 Multi-Dimensional Scaling (MDS) -- 5.2.1.1 Classical MDS: Spectral Approach -- 5.2.1.2 Metric MDS: Optimization-Based Approach -- 5.2.2 Isometric Mapping (ISOMAP) -- 5.2.3 t-Stochastic Neighbor Embedding ( t-SNE ) -- 5.3 Exploiting Randomness (RND) -- References -- 6 Information-Theoretic Approaches -- 6.1 Shannon Entropy (H) -- 6.2 Reduction by Conditional Entropy -- 6.3 Reduction by Iterated Conditional Entropy -- 6.4 Reduction by Conditional Entropy on Targets -- 6.5 Other Variations -- References -- 7 Molecular Computing Approaches -- 7.1 Encoding Abiotic Data into DNA -- 7.2 Deep Structure of DNA Spaces -- 7.2.1 Structural Properties of DNA Spaces -- 7.2.2 Noncrosshybridizing (nxh) Bases -- 7.3 Reduction by Genomic Signatures -- 7.3.1 Background -- 7.3.2 Genomic Signatures -- 7.4 Reduction by Pmeric Signatures -- References -- 8 Statistical Learning Approaches -- 8.1 Reduction by Multiple Regression -- 8.2 Reduction by Ridge Regression -- 8.3 Reduction by Lasso Regression -- 8.4 Selection Versus Shrinkage -- 8.5 Further Refinements -- References -- 9 Machine Learning Approaches -- 9.1 Autoassociative Feature Encoders -- 9.1.1 Undercomplete Autoencoders. 327 $a9.1.2 Sparse Autoencoders -- 9.1.3 Variational Autoencoders -- 9.1.4 Dimensionality Reduction in MNIST Images -- 9.2 Neural Feature Selection -- 9.2.1 Facial Features, Expressions, and Displays -- 9.2.2 The Cohn-Kanade Dataset -- 9.2.3 Primary and Derived Features -- 9.3 Other Methods -- References -- 10 Metaheuristics of DR Methods -- 10.1 Exploiting Feature Grouping -- 10.2 Exploiting Domain Knowledge -- 10.2.1 What Is Domain Knowledge? -- 10.2.2 Domain Knowledge for Dimensionality Reduction -- 10.3 Heuristic Rules for Feature Selection, Extraction, and Number -- 10.4 About Explainability of Solutions -- 10.4.1 What Is Explainability? -- 10.4.1.1 Outcome Explanations -- 10.4.1.2 Model Explanations -- 10.4.2 Explainability in Dimensionality Reduction -- 10.5 Choosing Wisely -- 10.6 About the Curse of Dimensionality -- 10.7 About the No-Free-Lunch Theorem (NFL) -- References -- 11 Appendices -- 11.1 Statistics and Probability Background -- 11.1.1 Commonly Used Discrete Distributions -- 11.1.2 Commonly Used Continuous Distributions -- 11.1.3 Major Results in Probability and Statistics -- 11.2 Linear Algebra Background -- 11.2.1 Fields, Vector Spaces and Subspaces -- 11.2.2 Linear Independence, Bases and Dimension -- 11.2.3 Linear Transformations and Matrices -- 11.2.4 Eigenvalues and Spectral Decomposition -- 11.3 Computer Science Background -- 11.3.1 Computational Science and Complexity -- 11.3.2 Machine Learning -- 11.4 Typical Data Science Problems -- 11.5 A Sample of Common and Big Datasets -- 11.6 Computing Platforms -- 11.6.1 The Environment R -- 11.6.2 Python Environments -- References. 606 $aBig data 606 $aDades massives$2thub 606 $aMineria de dades$2thub 606 $aData mining$xComputer programs 608 $aLlibres electrònics$2thub 615 0$aBig data. 615 7$aDades massives 615 7$aMineria de dades 615 0$aData mining$xComputer programs. 676 $a005.7 702 $aGarzo?n$b Max 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996483154003316 996 $aDimensionality reduction in data science$92979500 997 $aUNISA LEADER 02497nam 2200601 450 001 9910140123203321 005 20230125200848.0 010 $a2-7226-0338-1 024 7 $a10.4000/books.cdf.3641 035 $a(CKB)2560000000352119 035 $a(SSID)ssj0001542049 035 $a(PQKBManifestationID)11863097 035 $a(PQKBTitleCode)TC0001542049 035 $a(PQKBWorkID)11535325 035 $a(PQKB)10995859 035 $a(WaSeSS)IndRDA00045601 035 $a(FrMaCLE)OB-cdf-3641 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/58996 035 $a(PPN)267931484 035 $a(EXLCZ)992560000000352119 100 $a20160829d2015 uy 0 101 0 $aeng 135 $aur||#|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 14$aThe science of materials $efrom materials discovered by chance to customized materials /$fYves Brechet 210 $cCollège de France$d2015 210 1$aParis, France :$cCollège de France,$d2015 210 4$d©2015 215 $a1 online resource (55 pages) $cillustrations 225 1 $aLec?ons inaugurales du Colle?ge de France 320 $aIncludes bibliographical references. 330 $aThroughout the ages, humans have applied knowledge and know-how to master materials. They have gone from materials encountered by chance available in their environment to customized materials designed to meet multi-criteria specifications. Today, owing particularly to digital modelling on different scales, we are able to design high-performance materials, combining various classes of materials, in controlled geometries and dimensions. These innovation strategies - architectured or bio-inspired materials - have been integrated in many industrial sectors (cars, aeronautics, biomedical sciences, etc.). 410 0$aLec?ons inaugurales du Colle?ge de France. 606 $aMaterials$vInnovations 606 $aChemical Engineering 606 $aEngineering$vInnovations 606 $aMaterials Science 610 $amaterials 610 $amaterials science 610 $atechnological innovation 615 0$aMaterials 615 0$aChemical Engineering. 615 0$aEngineering 615 0$aMaterials Science. 700 $aBréchet$b Yves$0802316 801 0$bPQKB 801 2$bUkMaJRU 906 $aBOOK 912 $a9910140123203321 996 $aThe science of materials$92116861 997 $aUNINA LEADER 01900nam 22003853 450 001 9910795246703321 005 20230629225104.0 010 $a1-78914-485-X 035 $a(CKB)4940000000610337 035 $a(MiAaPQ)EBC6716019 035 $a(Au-PeEL)EBL6716019 035 $a(BIP)081428170 035 $a(EXLCZ)994940000000610337 100 $a20210901d2021 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aPostcards $eThe Rise and Fall of the World's First Social Network 210 1$aLondon :$cReaktion Books, Limited,$d2021. 210 4$d©2021. 215 $a1 online resource (233 pages) 311 $a1-78914-484-1 330 8 $aA global exploration of postcards as artifacts at the intersection of history, science, technology, art, and culture.Postcards are usually associated with banal holiday pleasantries, but they are made possible by sophisticated industries and institutions, from printers to postal services. When they were invented, postcards established what is now taken for granted in modern times: the ability to send and receive messages around the world easily and inexpensively. Fundamentally they are about creating personal connections--links between people, places, and beliefs. Lydia Pyne examines postcards on a global scale, to understand them as artifacts that are at the intersection of history, science, technology, art, and culture. In doing so, she shows how postcards were the first global social network and also, here in the twenty-first century, how postcards are not yet extinct. 517 $aPostcards 676 $a741.683 700 $aPyne$b Lydia$01252628 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910795246703321 996 $aPostcards$93700780 997 $aUNINA