Nonlinear Dimensionality Reduction Techniques - Benoit Colange

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Auteur(s) : Benoit Colange - Denys Dutykh - Sylvain Lespinats

Editeur : Springer International Publishing Ag

Langue : Anglais

Parution : 01/12/2021

Format : Moyen, de 350g à 1kg

Nombre de pages : 292

Expédition : 606

Dimensions : 24.1 x 16.0 x 2.2

ISBN : 9783030810252

Résumé :
This book proposes tools for analysis of multidimensional and metric data, by establishing a state-of-the-art of the existing solutions and developing new ones. It mainly focuses on visual exploration of these data by a human analyst, relying on a 2D or 3D scatter plot display obtained through Dimensionality Reduction.

Performing diagnosis of an energy system requires identifying relations between observed monitoring variables and the associated internal state of the system. Dimensionality reduction, which allows to represent visually a multidimensional dataset, constitutes a promising tool to help domain experts to analyse these relations. This book reviews existing techniques for visual data exploration and dimensionality reduction such as tSNE and Isomap, and proposes new solutions to challenges in that field.

In particular, it presents the new unsupervised technique ASKI and the supervised methods ClassNeRV and ClassJSE. Moreover, MING, a new approach for local map quality evaluation is also introduced. These methods are then applied to the representation of expert-designed fault indicators for smart-buildings, I-V curves for photovoltaic systems and acoustic signals for Li-ion batteries.

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Biographie:
After his PhD degree in biomathematics from Pierre and Marie Curie University, Sylvain Lespinats held postdoc positions at several institutions, including INSERM (the French National Institute of Medical Reseach), INREST (the French National Insistute of Transport and Security Research), and some universities and research institutes. He is currently a permanent researcher at CEA-INES (the French National Institute of Solar Energy) near Chambery. He is the author or co-author of about 50 papers and more then ten patents. His work is dedicated to providing ad hoc approaches for data mining and knowledge discovery to his colleagues in various fields, including genomics, virology, quantitiative sociology, transport security, solar energy forecasting, solar plang security, and battery diagnosis. Dr. Lespinats's scientific interests include the exhibition of spatial structures in high dimensional data. In that framework, he developed several non-linear mapping methods and workedon the local evaluation of mappings. Recently he mainly focuses on renewable data to contribute to energy transition.

Sommaire:
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 A...