Bartkowiak, Anna and Trendafilov, Nickolay
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Derivation of new features of observed variables has two important goals: reduction of dimensionality and de-noising. A desired property of the derived new features is their meaningful interpretation. The SCoTLASS method (Jolliffe, Trendafilov and Uddin, 2003) offers such possibility.
We explore the preoperties of the SCoTLASS method applied to the yeast genes data investicaged in (Bartkowiak et al., 2003, 2004). All the derived features have really a simple meaningful structue: each new feature is spanned by two original variables belonging to the same block.
|Item Type:||Book Chapter|
|Copyright Holders:||2005 Springer-Verlag|
|Extra Information:||Proceedings of the International IIS: IIPWM´05 Conference held in Gdansk, Poland, June 13-16, 2005
|Keywords:||artificial intelligence; data mining; machine learning|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Depositing User:||Sarah Frain|
|Date Deposited:||07 Sep 2010 09:07|
|Last Modified:||04 Oct 2016 10:41|
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