NDLI: Supervised clustering via principal component analysis in a retrieval application

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Supervised clustering via principal component analysis in a retrieval application

Content Provider	ACM Digital Library
Author	Garcia-Cuesta, Esteban Galvan, Ines M. de Castro, Antonio J.
Abstract	In regression problems where the number of predictors exceeds the number of observations and the correlation between the predictors is high, a dimensionality reduction or a variable selection approach is demanded. In this paper we deal with a real application where we want to retrieve the physical characteristics of a combustion process from the measurements obtained with a spectroscopic sensor. This application shows up a multicollinearity problem but furthermore it is considered an ill-posed problem. Guided by this application scenario, we propose a clustering approach to find out homogeneous subsets of data which are embedded in arbitrary oriented linear manifold. This model is developed under certain assumptions guided by a priori problem knowledge. The resulting division preserves both, the priori assumptions and the homogeneity in the models. Thereby we break the whole problem in n subproblems improving its individual prediction accuracy versus a global solution. We show the obtained improvements in a real application scenario related with estimating the temperature from spectroscopic data in a remote sensing framework.
Starting Page	97
Ending Page	104
Page Count	8
File Format	PDF
ISBN	9781605586687
DOI	10.1145/1601966.1601983
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-06-28
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Dimensionality reduction Supervised learning Regression Principal components analysis Clustering Remote sensing
Content Type	Text
Resource Type	Article

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