NDLI: Dimensionality Reduction and Feature Extraction and Classification

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Dimensionality Reduction and Feature Extraction and Classification

Content Provider	Scilit
Author	Messina, Arturo Román
Copyright Year	2020
Description	Dimensionality reduction has been an active field of research in power system analysis and control in recent years. Low-dimensional representations are of interest to provide a better understanding of the involved physical phenomena and can be used for feature extraction and classification, data visualization, and prognostics. These representations have been shown to be useful in the analysis and characterization of the global behavior of transient processes, as well as to extract and isolate the most dominant modes of motion for tracking and monitoring power system health. In this chapter, a general framework for dimensionality reduction of high-dimensional data is proposed. Methods for analysis and dimensionality reduction of large, complex data sets are developed and a physical interpretation is provided. Applications to clustering and classification of observational data are discussed. Open problems in dimensionality reduction are also reviewed and factors that affect the performance of the method are discussed, including the effects of non-linear trends, and missing data. Book Name: Data Fusion and Data Mining for Power System Monitoring
Related Links	https://content.taylorfrancis.com/books/download?dac=C2019-0-00355-9&isbn=9780429319440&doi=10.1201/9780429319440-8&format=pdf
Ending Page	149
Page Count	29
Starting Page	121
DOI	10.1201/9780429319440-8
Language	English
Publisher	Informa UK Limited
Publisher Date	2020-05-05
Access Restriction	Open
Subject Keyword	Book Name: Data Fusion and Data Mining for Power System Monitoring Aerospace Engineering Behavior Feature Extraction Dimensionality Reduction Representations Power System Extraction and Classification
Content Type	Text
Resource Type	Chapter

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