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Nonlinear Dimensionality Reduction Techniques

A Data Structure Preservation Approach

Language EnglishEnglish
Book Paperback
Book Nonlinear Dimensionality Reduction Techniques Sylvain Lespinats
Libristo code: 42081852
Publishers Springer, Berlin, November 2021
This book proposes tools for analysis of multidimensional and metric data, by establishing a state-o... Full description
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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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About the book

Full name Nonlinear Dimensionality Reduction Techniques
Language English
Binding Book - Paperback
Date of issue 2022
Number of pages 247
EAN 9783030810283
Libristo code 42081852
Publishers Springer, Berlin
Weight 450
Dimensions 155 x 235 x 16
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