Nonlinear Dimensionality Reduction Techniques: A Data Structure Preservation Approach

By Sylvain Lespinats, Denys Dutykh, Benoit Colange

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.

• Format: Hardcover
• Language: English
• ISBN: 3030810259
• ISBN13: 9783030810252
• Release Date: December 2021
• Publisher: Springer
• Length: 247 Pages
• Weight: 1.29 lbs.
• Dimensions: 0.7" x 6.1" x 9.2"