|Title:||Dimension Reduction||Authors:||Cunningham, Pádraig||Permanent link:||http://hdl.handle.net/10197/12350||Date:||8-Aug-2007||Online since:||2021-07-28T11:19:49Z||Abstract:||When data objects that are the subject of analysis using machine learning techniques are described by a large number of features (i.e. the data is high dimension) it is often beneficial to reduce the dimension of the data. Dimension reduction can be beneficial not only for reasons of computational efficiency but also because it can improve the accuracy of the analysis. The set of techniques that can be employed for dimension reduction can be partitioned in two important ways; they can be separated into techniques that apply to supervised or unsupervised learning and into techniques that either entail feature selection or feature extraction. In this paper an overview of dimension reduction techniques based on this organisation is presented and representative techniques in each category is described.||Type of material:||Technical Report||Publisher:||University College Dublin. School of Computer Science and Informatics||Series/Report no.:||UCD CSI Technical Reports; UCD-CSI-2007-7||Copyright (published version):||2007 the Author||Keywords:||Data analysis; Machine learning; Pattern recognition; High dimension data; Curse of dimensionality; Feature transformation; Feature selection||Language:||en||Status of Item:||Not peer reviewed||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Computer Science and Informatics Technical Reports|
Show full item record
If you are a publisher or author and have copyright concerns for any item, please email firstname.lastname@example.org and the item will be withdrawn immediately. The author or person responsible for depositing the article will be contacted within one business day.