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Dimensionality Reduction with Unsupervised Nearest Neighbors

ISRL: DIMENSIONALITY RDCTN W/UNSPRVSD NEAREST NEIGHBORS

Information Technology

Computing & Information Technology

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B2B GT

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B2B & B2C

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. 

POST SECONDARY FOUR YEAR

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COLJR

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Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Editorial Board.
Title Page.
Copyright Page.
Abstract.
Acknowledgements.
Contents.
List of Figures.
List of Tables.
Introduction.
1: Foundations.
2: K-Nearest Neighbors.
3: Ensemble Learning.
4: Dimensionality Reduction.
5: Unsupervised Nearest Neighbors.
6: Latent Sorting.
7: Metaheuristics.
8: Kernel and Submanifold Learning.
9: Conclusions.
10: Summary and Outlook.
11: Test Problems.
References.
Index.

Dimensionality Reduction with Unsupervised Nearest Neighbors.css-1p8rvzy{position:absolute;padding-left:8px;}

Dimensionality Reduction with Unsupervised Nearest Neighbors