Nonlinear Dimensionality Reduction Techniques
portes grátis
Nonlinear Dimensionality Reduction Techniques
A Data Structure Preservation Approach
Lespinats, Sylvain; Dutykh, Denys; Colange, Benoit
Springer Nature Switzerland AG
12/2021
247
Dura
Inglês
9783030810252
15 a 20 dias
606
Descrição não disponível.
1 Data science context.- 1.1 Data in a metric space.- 1.1.1 Measuring dissimilarities and similarities .- 1.1.2 Neighbourhood ranks.- 1.1.3 Embedding space notations.- 1.1.4 Multidimensional data .- 1.1.5 Sequence data.- 1.1.6 Network data.- 1.1.7 A few multidimensional datasets .- 1.2 Automated tasks.- 1.2.1 Underlying distribution.- 1.2.2 Category identification.- 1.2.3 Data manifold analysis.- 1.2.4 Model learning.- 1.2.5 Regression.- 1.3 Visual exploration.- 1.3.1 Human in the loop using graphic variables.- 1.3.2 Spatialization and Gestalt principles.- 1.3.3 Scatter plots.- 1.3.4 Parallel coordinates.- 1.3.5 Colour coding.- 1.3.6 Multiple coordinated views and visual interaction.- 1.3.7 Graph drawing.- 2 Intrinsic dimensionality.- 2.1 Curse of dimensionality.- 2.1.1 Data sparsity.- 2.1.2 Norm concentration.- 2.2 ID estimation.- 2.2.1 Covariance-based approaches.- 2.2.2 Fractal approaches.- 2.2.3 Towards local estimation.- 2.3 TIDLE .- 2.3.1 Gaussian mixture modelling.- 2.3.2 Test of TIDLE on a two clusters case.- 3 Map evaluation.- 3.1 Objective and practical indicators.- 3.1.1 Subjectivity of indicators.- 3.1.2 User studies on specific tasks.- 3.2 Unsupervised global evaluation.- 3.2.1 Types of distortions.- 3.2.2 Link between distortions and mapping continuity.- 3.2.3 Reasons of distortions ubiquity.- 3.2.4 Scalar indicators.- 3.2.5 Aggregation.- 3.2.6 Diagrams.- 3.3 Class-aware indicators.- 3.3.1 Class separation and aggregation.- 3.3.2 Comparing scores between the two spaces.- 3.3.3 Class cohesion and distinction.- 3.3.4 The case of one cluster per class.- 4 Map interpretation.- 4.1 Axes recovery.- 4.1.1 Linear case: biplots .- 4.1.2 Non-linear case.- 4.2 Local evaluation.- 4.2.1 Point-wise aggregation.- 4.2.2 One to many relations with focus point.- 4.2.3 Many to many relations.- 4.3 MING.- 4.3.1 Uniform formulation of rank-based indicator.- 4.3.2 MING graphs.- 4.3.3 MING analysis for a toy dataset.- 4.3.4 Impact of MING parameters.- 4.3.5 Visual clutter.- 4.3.6 Oil flow.- 4.3.7 COIL-20 dataset.- 4.3.8 MING perspectives.- 5 Unsupervised DR.- 5.1 Spectral projections.- 5.1.1 Principal Component Analysis.- 5.1.2 Classical MultiDimensional Scaling.- 5.1.3 Kernel methods: Isompap, KPCA, LE.- 5.2 Non-linear MDS.- 5.2.1 Metric MultiDimensional Scaling.- 5.2.2 Non-metric MultiDimensional Scaling.- 5.3 Neighbourhood Embedding.- 5.3.1 General principle: SNE.- 5.3.2 Scale setting.- 5.3.3 Divergence choice: NeRV and JSE.- 5.3.4 Symmetrization.- 5.3.5 Solving the crowding problem: tSNE.- 5.3.6 Kernel choice.- 5.3.7 Adaptive Student Kernel Imbedding.- 5.4 Graph layout.- 5.4.1 Force directed graph layout: Elastic Embedding.- 5.4.2 Probabilistic graph layout: LargeVis.- 5.4.3 Topological method UMAP.- 5.5 Artificial neural networks.- 5.5.1 Auto-encoders.- 5.5.2 IVIS.- 6 Supervised DR.- 6.1 Types of supervision.- 6.1.1 Full supervision.- 6.1.2 Weak supervision.- 6.1.3 Semi-supervision.- 6.2 Parametric with class purity.- 6.2.1 Linear Discriminant Analysis.- 6.2.2 Neighbourhood Component Analysis.- 6.3 Metric learning.- 6.3.1 Mahalanobis distances.- 6.3.2 Riemannian metric.- 6.3.3 Direct distances transformation.- 6.3.4 Similarities learning.- 6.3.5 Metric learning limitations.- 6.4 Class adaptive scale.- 6.5 Classimap.- 6.6 CGNE.- 6.6.1 ClassNeRV stress.- 6.6.2 Flexibility of the supervision.- 6.6.3 Ablation study.- 6.6.4 Isolet 5 case study.- 6.6.5 Robustness to class misinformation.- 6.6.6 Extension to the type 2 mixture: ClassJSE.- 6.6.7 Extension to semi-supervision and weak-supervision.- 6.6.8 Extension to soft labels.- 7 Mapping construction.- 7.1 Optimization.- 7.1.1 Global and local optima.- 7.1.2 Descent algorithms.- 7.1.3 Initialization.- 7.1.4 Multi-scale optimization.- 7.1.5 Force-directed placement interpretation.- 7.2 Acceleration strategies.- 7.2.1 Attractive forces approximation.- 7.2.2 Binary search trees.- 7.2.3 Repulsive forces.- 7.2.4 Landmarks approximation.- 7.3 Out of sample extension.- 7.3.1 Applications.- 7.3.2 Parametric case .- 7.3.3 Non-parametric stress with neural network model.- 7.3.4 Non-parametric case.- 8 Applications.- 8.1 Smart buildings commissioning.- 8.1.1 System and rules.- 8.1.2 Mapping.- 8.2 Photovoltaics.- 8.2.1 I-V curves.- 8.2.2 Comparing normalized I-V curves.- 8.2.3 Colour description of the chemical compositions.- 8.3 Batteries.- 8.3.1 Case 1 1.- 8.3.2 Case 2 2.- 9 Conclusions.- Nomenclature.- A Some technical results.- A.1 Equivalence between triangle inequality and convexity of balls for.- a pseudo-norm.- A.2 From Pareto to exponential distribution.- A.3 Spiral and Swiss roll.- B Kullback-Leibler divergence.- B.1 Generalized Kullback-Leibler divergence.- B.1.1 Perplexity with hard neighbourhoods.- B.2 Link between soft and hard recall and precision.- Details of calculations.- C.1 General gradient of stress function.- C.2 Neighbourhood embedding.- C.2.1 Supervised neighbourhood embedding (asymmetric case).- C.2.2 Mixtures.- C.2.3 Belonging rates .- C.2.4 Soft-min arguments.- C.2.5 Scale setting by perplexity.- C.2.6 Force interpretation.- D Spectral projections algebra.- D.1 PCA as matrix factorization and SVD resolution.- D.2 Link with linear projection.- D.3 Sparse expression.- D.4 PCA and centering: from affine to linear.- D.5 Link with covariance and Gram matrices.- D.6 From distances to Gram matrix.- D.6.1 Probabilistic interpretation and maximum likelihood.- D.7 Nystroem approximation.- References .- Index 7.
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dimensionality reduction;data mining;intrinsic dimensionality;mapping evaluation;high dimensional data;visual analytics
1 Data science context.- 1.1 Data in a metric space.- 1.1.1 Measuring dissimilarities and similarities .- 1.1.2 Neighbourhood ranks.- 1.1.3 Embedding space notations.- 1.1.4 Multidimensional data .- 1.1.5 Sequence data.- 1.1.6 Network data.- 1.1.7 A few multidimensional datasets .- 1.2 Automated tasks.- 1.2.1 Underlying distribution.- 1.2.2 Category identification.- 1.2.3 Data manifold analysis.- 1.2.4 Model learning.- 1.2.5 Regression.- 1.3 Visual exploration.- 1.3.1 Human in the loop using graphic variables.- 1.3.2 Spatialization and Gestalt principles.- 1.3.3 Scatter plots.- 1.3.4 Parallel coordinates.- 1.3.5 Colour coding.- 1.3.6 Multiple coordinated views and visual interaction.- 1.3.7 Graph drawing.- 2 Intrinsic dimensionality.- 2.1 Curse of dimensionality.- 2.1.1 Data sparsity.- 2.1.2 Norm concentration.- 2.2 ID estimation.- 2.2.1 Covariance-based approaches.- 2.2.2 Fractal approaches.- 2.2.3 Towards local estimation.- 2.3 TIDLE .- 2.3.1 Gaussian mixture modelling.- 2.3.2 Test of TIDLE on a two clusters case.- 3 Map evaluation.- 3.1 Objective and practical indicators.- 3.1.1 Subjectivity of indicators.- 3.1.2 User studies on specific tasks.- 3.2 Unsupervised global evaluation.- 3.2.1 Types of distortions.- 3.2.2 Link between distortions and mapping continuity.- 3.2.3 Reasons of distortions ubiquity.- 3.2.4 Scalar indicators.- 3.2.5 Aggregation.- 3.2.6 Diagrams.- 3.3 Class-aware indicators.- 3.3.1 Class separation and aggregation.- 3.3.2 Comparing scores between the two spaces.- 3.3.3 Class cohesion and distinction.- 3.3.4 The case of one cluster per class.- 4 Map interpretation.- 4.1 Axes recovery.- 4.1.1 Linear case: biplots .- 4.1.2 Non-linear case.- 4.2 Local evaluation.- 4.2.1 Point-wise aggregation.- 4.2.2 One to many relations with focus point.- 4.2.3 Many to many relations.- 4.3 MING.- 4.3.1 Uniform formulation of rank-based indicator.- 4.3.2 MING graphs.- 4.3.3 MING analysis for a toy dataset.- 4.3.4 Impact of MING parameters.- 4.3.5 Visual clutter.- 4.3.6 Oil flow.- 4.3.7 COIL-20 dataset.- 4.3.8 MING perspectives.- 5 Unsupervised DR.- 5.1 Spectral projections.- 5.1.1 Principal Component Analysis.- 5.1.2 Classical MultiDimensional Scaling.- 5.1.3 Kernel methods: Isompap, KPCA, LE.- 5.2 Non-linear MDS.- 5.2.1 Metric MultiDimensional Scaling.- 5.2.2 Non-metric MultiDimensional Scaling.- 5.3 Neighbourhood Embedding.- 5.3.1 General principle: SNE.- 5.3.2 Scale setting.- 5.3.3 Divergence choice: NeRV and JSE.- 5.3.4 Symmetrization.- 5.3.5 Solving the crowding problem: tSNE.- 5.3.6 Kernel choice.- 5.3.7 Adaptive Student Kernel Imbedding.- 5.4 Graph layout.- 5.4.1 Force directed graph layout: Elastic Embedding.- 5.4.2 Probabilistic graph layout: LargeVis.- 5.4.3 Topological method UMAP.- 5.5 Artificial neural networks.- 5.5.1 Auto-encoders.- 5.5.2 IVIS.- 6 Supervised DR.- 6.1 Types of supervision.- 6.1.1 Full supervision.- 6.1.2 Weak supervision.- 6.1.3 Semi-supervision.- 6.2 Parametric with class purity.- 6.2.1 Linear Discriminant Analysis.- 6.2.2 Neighbourhood Component Analysis.- 6.3 Metric learning.- 6.3.1 Mahalanobis distances.- 6.3.2 Riemannian metric.- 6.3.3 Direct distances transformation.- 6.3.4 Similarities learning.- 6.3.5 Metric learning limitations.- 6.4 Class adaptive scale.- 6.5 Classimap.- 6.6 CGNE.- 6.6.1 ClassNeRV stress.- 6.6.2 Flexibility of the supervision.- 6.6.3 Ablation study.- 6.6.4 Isolet 5 case study.- 6.6.5 Robustness to class misinformation.- 6.6.6 Extension to the type 2 mixture: ClassJSE.- 6.6.7 Extension to semi-supervision and weak-supervision.- 6.6.8 Extension to soft labels.- 7 Mapping construction.- 7.1 Optimization.- 7.1.1 Global and local optima.- 7.1.2 Descent algorithms.- 7.1.3 Initialization.- 7.1.4 Multi-scale optimization.- 7.1.5 Force-directed placement interpretation.- 7.2 Acceleration strategies.- 7.2.1 Attractive forces approximation.- 7.2.2 Binary search trees.- 7.2.3 Repulsive forces.- 7.2.4 Landmarks approximation.- 7.3 Out of sample extension.- 7.3.1 Applications.- 7.3.2 Parametric case .- 7.3.3 Non-parametric stress with neural network model.- 7.3.4 Non-parametric case.- 8 Applications.- 8.1 Smart buildings commissioning.- 8.1.1 System and rules.- 8.1.2 Mapping.- 8.2 Photovoltaics.- 8.2.1 I-V curves.- 8.2.2 Comparing normalized I-V curves.- 8.2.3 Colour description of the chemical compositions.- 8.3 Batteries.- 8.3.1 Case 1 1.- 8.3.2 Case 2 2.- 9 Conclusions.- Nomenclature.- A Some technical results.- A.1 Equivalence between triangle inequality and convexity of balls for.- a pseudo-norm.- A.2 From Pareto to exponential distribution.- A.3 Spiral and Swiss roll.- B Kullback-Leibler divergence.- B.1 Generalized Kullback-Leibler divergence.- B.1.1 Perplexity with hard neighbourhoods.- B.2 Link between soft and hard recall and precision.- Details of calculations.- C.1 General gradient of stress function.- C.2 Neighbourhood embedding.- C.2.1 Supervised neighbourhood embedding (asymmetric case).- C.2.2 Mixtures.- C.2.3 Belonging rates .- C.2.4 Soft-min arguments.- C.2.5 Scale setting by perplexity.- C.2.6 Force interpretation.- D Spectral projections algebra.- D.1 PCA as matrix factorization and SVD resolution.- D.2 Link with linear projection.- D.3 Sparse expression.- D.4 PCA and centering: from affine to linear.- D.5 Link with covariance and Gram matrices.- D.6 From distances to Gram matrix.- D.6.1 Probabilistic interpretation and maximum likelihood.- D.7 Nystroem approximation.- References .- Index 7.
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