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Springer Nonlinear Dimensionality Reduction Techniques: A Data Structure Preservation Approach

Name: Nonlinear Dimensionality Reduction Techniques: A Data Structure Preservation Approach
Brand: Springer

Sd-mo26

AI & Machine Learning

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Description

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.

Product Specifications

Brand: Springer
Colour: Sd-mo26
Size: 5' X 5'
Format: paperback
ASIN: 3030810283
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 04 December 2022
Listed Since: 19 August 2020

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Springer Nonlinear Dimensionality Reduction Techniques: A Data Structure Preservation Approach

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