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Dimensionality Reduction with Unsupervised Nearest Neighbors [electronic resource] / by Oliver Kramer.

By: Kramer, Oliver [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Intelligent Systems Reference Library: 51Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Description: XII, 132 p. 48 illus., 45 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642386527.Subject(s): Engineering | Operations research | Decision making | Artificial intelligence | Applied mathematics | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics) | Operation Research/Decision TheoryAdditional physical formats: Printed edition:: No titleDDC classification: 519 Online resources: Click here to access online
Contents:
Part I Foundations -- Part II Unsupervised Nearest Neighbors -- Part III Conclusions.
In: Springer eBooksSummary: This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustrate the introduced concepts and to highlight the experimental results.  .
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Part I Foundations -- Part II Unsupervised Nearest Neighbors -- Part III Conclusions.

This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustrate the introduced concepts and to highlight the experimental results.  .

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