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Nonlinear Dimensionality Reduction Techniques A Data Structure Preservation Approach 1st Edition by Sylvain Lespinats, Benoit Colange, Denys Dutykh ISBN 9783030810252 3030810259

Name: Nonlinear Dimensionality Reduction Techniques A Data Structure Preservation Approach 1st Edition by Sylvain Lespinats, Benoit Colange, Denys Dutykh ISBN 9783030810252 3030810259
SKU: EB-36474056
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Nonlinear Dimensionality Reduction Techniques: A Data Structure Preservation Approach Lespinats Digital Instant Download

Author(s): Lespinats, Sylvain, Colange, Benoit, Dutykh, Denys

ISBN(s): 9783030810252, 3030810259

Edition: 1st ed. 2022

File Details: PDF, 16.98 MB

Year: 2021

Language: English

SKU: EB-36474056 Category: Uncategorized Tags: Benoit, Colange, Denys, Dutykh, Lespinats, Sylvain

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Nonlinear Dimensionality Reduction Techniques A Data Structure Preservation Approach 1st Edition by Sylvain Lespinats, Benoit Colange, Denys Dutykh – Ebook PDF Instant Download/Delivery: 9783030810252 ,3030810259
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Product details:
ISBN 10: 3030810259
ISBN 13: 9783030810252
Author: Sylvain Lespinats, Benoit Colange, Denys Dutykh

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.

Nonlinear Dimensionality Reduction Techniques A Data Structure Preservation Approach 1st Edition Table of contents:

1. Data Science Context

2. Intrinsic Dimensionality

3. Map Evaluation

4. Map Interpretation

5. Stress Functions for Unsupervised Dimensionality Reduction

6. Stress Functions for Supervised Dimensionality Reduction

7. Optimization, Acceleration and Out of Sample Extensions

8. Applications of Dimensionality Reduction to the Diagnosis of Energy Systems

9. Conclusions

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Tags: Sylvain Lespinats, Benoit Colange, Denys Dutykh, Nonlinear Dimensionality, Reduction Techniques, Data Structure