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.

 

Title: Nonlinear Dimensionality Reduction Techniques: A Data Structure Preservation Approach Author(s): Sylvain Lespinats, Benoit Colange, Denys Dutykh Publisher: Springer Year: 2022 ISBN: 3030810259; 9783030810252

 

Title:	Nonlinear Dimensionality Reduction Techniques: A Data Structure Preservation Approach		Volume:
Author(s):	Sylvain Lespinats, Benoit Colange, Denys Dutykh
Series:		Periodical:
Publisher:	Springer	City:
Year:	2022	Edition:	1
Language:	English	Pages (biblio ech):	290
ISBN:	3030810259, 9783030810252	ID:	3151668
Time added:	2021-12-02 09:50:49	Time modified:	2021-12-02 17:42:00
Library:		Library issue:
Size:	38 MB (39815504 bytes)	Extension:	epub