Dimensionality Reduction with Unsupervised Nearest Neighbors
Kramer, Oliver.
Dimensionality Reduction with Unsupervised Nearest Neighbors [electronic resource] / by Oliver Kramer. - XII, 132 p. 48 illus., 45 illus. in color. online resource. - Intelligent Systems Reference Library, 51 1868-4394 ; . - Intelligent Systems Reference Library, 51 .
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. .
9783642386527
10.1007/978-3-642-38652-7 doi
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 Theory.
TA329-348 TA640-643
519
Dimensionality Reduction with Unsupervised Nearest Neighbors [electronic resource] / by Oliver Kramer. - XII, 132 p. 48 illus., 45 illus. in color. online resource. - Intelligent Systems Reference Library, 51 1868-4394 ; . - Intelligent Systems Reference Library, 51 .
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. .
9783642386527
10.1007/978-3-642-38652-7 doi
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 Theory.
TA329-348 TA640-643
519