| 书目名称 | Nonlinear Dimensionality Reduction Techniques | | 副标题 | A Data Structure Pre | | 编辑 | Sylvain Lespinats,Benoit Colange,Denys Dutykh | | 视频video | | | 概述 | Reviews state-of-the-art methods in dimensionality reduction techniques, written in a clear but precise mathematical language.Presents application of the methods to the representation of expert-design | | 图书封面 |  | | 描述 | 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- | | 出版日期 | Book 2022 | | 关键词 | dimensionality reduction; data mining; intrinsic dimensionality; mapping evaluation; high dimensional da | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-81026-9 | | isbn_softcover | 978-3-030-81028-3 | | isbn_ebook | 978-3-030-81026-9 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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发表于 2025-3-21 18:24:02
