| Description |
1 online resource |
| Series |
Information Fusion and Data Science |
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Information Fusion and Data Science
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| Contents |
Intro -- Preface -- Contents -- Notation -- Chapter 1: A Gentle Introduction to Feature Learning -- 1.1 Introduction -- 1.2 Data and Preprocessing -- 1.2.1 Data Collection -- 1.2.2 Data Cleaning -- 1.2.3 Data Sampling -- 1.2.4 Data Transformation -- 1.3 Feature Learning -- 1.3.1 Solutions to Eigenvalue Equations -- 1.3.2 Convex Optimization -- 1.3.3 Gradient Descent -- 1.4 Summary -- Chapter 2: Latent Semantic Feature Extraction -- 2.1 Introduction -- 2.2 Singular Value Decomposition -- 2.2.1 Feature Extraction by SVD -- 2.2.2 An Example of SVD -- 2.3 SVD Updating |
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2.4 SVD with Compressive Sampling -- 2.5 Case Studies -- 2.5.1 Analysis of Coil-20 Data Set -- 2.5.2 Latent Semantic Feature Extraction for Recommendation -- 2.6 Summary -- Chapter 3: Principal Component Analysis -- 3.1 Introduction -- 3.2 Classical Principal Component Analysis -- 3.2.1 Maximizing Variance and Minimizing Residuals -- 3.2.2 Theoretical Derivation of PCA -- 3.2.3 An Alternative View of PCA -- 3.2.4 Selection of the Reduced Dimension -- 3.2.5 Eigendecomposition of XXT or XTX -- 3.2.6 Relationship between PCA and SVD -- 3.3 Probabilistic Principal Component Analysis |
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3.3.1 Latent Variable Model -- 3.3.2 The Probability Model of PPCA -- 3.3.3 The Maximum Likelihood Estimation of PPCA -- 3.3.4 The PPCA Algorithm -- 3.4 Case Studies -- 3.4.1 Enterprise Profit Ratio Analysis Using PCA -- 3.4.2 Fault Detection Based on PCA -- 3.5 Summary -- Chapter 4: Manifold-Learning-Based Feature Extraction -- 4.1 Introduction -- 4.2 Manifold Learning and Spectral Graph Theory -- 4.3 Neighborhood Preserving Projection -- 4.3.1 Locally Linear Embedding (LLE) -- 4.3.2 Neighborhood Preserving Embedding (NPE) -- 4.4 Locality Preserving Projection (LPP) -- 4.4.1 Relationship to PCA |
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4.4.2 Relationship to Laplacian Eigenmaps -- 4.5 Case Studies -- 4.5.1 Handwritten Digit Visualization -- 4.5.2 Face Manifold Analysis -- 4.6 Summary -- Chapter 5: Linear Discriminant Analysis -- 5.1 Introduction -- 5.2 Fisherś Linear Discriminant -- 5.3 Analysis of FLD -- 5.4 Linear Discriminant Analysis -- 5.4.1 An Example of LDA -- 5.4.2 Foley-Sammon Optimal Discriminant Vectors -- 5.5 Case Study -- 5.6 Summary -- Chapter 6: Kernel-Based Nonlinear Feature Learning -- 6.1 Introduction -- 6.2 Kernel Trick -- 6.3 Kernel Principal Component Analysis -- 6.3.1 Revisiting of PCA |
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6.3.2 Derivation of Kernel Principal Component Analysis -- 6.3.3 Kernel Averaging Filter -- 6.4 Kernel Fisher Discriminant -- 6.5 Generalized Discriminant Analysis -- 6.6 Case Study -- 6.7 Summary -- Chapter 7: Sparse Feature Learning -- 7.1 Introduction -- 7.2 Sparse Representation Problem with Different Norm Regularizations -- 7.2.1 0-norm Regularized Sparse Representation -- 7.2.2 1-norm Regularized Sparse Representation -- 7.2.3 p-norm (0 <p <1) Regularized Sparse Representation -- 7.2.4 2,1-norm Regularized Group-Wise Sparse Representation -- 7.3 Lasso Estimator |
| Summary |
This book covers the essential concepts and strategies within traditional and cutting-edge feature learning methods thru both theoretical analysis and case studies. Good features give good models and it is usually not classifiers but features that determine the effectiveness of a model. In this book, readers can find not only traditional feature learning methods, such as principal component analysis, linear discriminant analysis, and geometrical-structure-based methods, but also advanced feature learning methods, such as sparse learning, low-rank decomposition, tensor-based feature extraction, and deep-learning-based feature learning. Each feature learning method has its own dedicated chapter that explains how it is theoretically derived and shows how it is implemented for real-world applications. Detailed illustrated figures are included for better understanding. This book can be used by students, researchers, and engineers looking for a reference guide for popular methods of feature learning and machine intelligence |
| Bibliography |
Includes bibliographical references and index |
| Subject |
Machine learning.
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Big data.
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Machine learning.
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Artificial intelligence.
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Pattern recognition.
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Imaging systems & technology.
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Image processing.
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Social research & statistics.
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Computers -- Intelligence (AI) & Semantics.
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Technology & Engineering -- Engineering (General)
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Computers -- Computer Vision & Pattern Recognition.
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Technology & Engineering -- Electronics -- General.
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Computers -- Computer Graphics.
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Science -- System Theory.
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Big data
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Machine learning
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| Form |
Electronic book
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| Author |
Zhao, Haitao, 1986-
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Lai, Zhihui
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Leung, Henry.
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Zhang, Xianyi
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| ISBN |
9783030407940 |
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3030407942 |
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