Dimensionality reduction with unsupervised nearest neighbors [electronic resource]
- 作者： Kramer, Oliver.
- 其他作者：
- 其他題名：
- Intelligent systems reference library ;
- 出版： Berlin, Heidelberg : Springer Berlin Heidelberg :Imprint: Springer
- 叢書名： Intelligent systems reference library,v.51
- 主題： Nearest neighbor analysis (Statistics) , Dimension reduction (Statistics) , Regression analysis--Mathematical models. , Data mining. , Engineering. , Appl.Mathematics/Computational Methods of Engineering. , Artificial Intelligence (incl. Robotics). , Operation Research/Decision Theory.
- ISBN： 9783642386527 (electronic bk.) 、 9783642386510 (paper)
-
FIND@SFXID:
CGU
- 資料類型: 電子書
-
讀者標籤：
- 系統號： 005394050 | 機讀編目格式
館藏資訊
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.