A Novel Multivariate Mapping Method for Analyzing High-Dimensional Numerical Datasets

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• 关键词：Feature selection ; Dimensionality reduction ; Density estimation
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9728
• 期：1
• 页码：311-319
• 全文大小：782 KB
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• 作者单位：Edwin Aldana-Bobadilla (14)
  Alejandro Molina-Villegas (15)
  
  14. CINVESTAV-Unidad Tamaulipas, Ciudad Victoria, Mexico
  15. The National Commission for Knowledge and Use of Biodiversity, Mexico City, Mexico
  
• 丛书名：Advances in Data Mining. Applications and Theoretical Aspects
• ISBN：978-3-319-41561-1
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9728

文摘

In modern science, dealing with high dimensional datasets is a very common task due to the increasing availability of data. Multivariate data analysis represents challenges in both theoretical and empirical levels. Until now, several methods for dimensionality reduction like Principal Component Analysis, Low Variance Filter and High Correlated Columns has been proposed. However, sometimes the reduction achieved by existing methods is not accurate enough to analyze datasets where, for practical reasons, more reduction of the original dataset is required. In this paper, we propose a new method to transform high dimensional dataset into a one-dimensional. We show that such transformation preserves the properties of the original dataset and thus, it can be suitable for many applications where a high reduction is required.