PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Assessment of clusters reliability for high dimensional genomic data
Alberto Bertoni, Raffaella Folgieri and Giorgio Valentini
In: BITS 2005, 17-19 Mar 2005, Milano, Italy.

Abstract

Discovering new subclasses of pathologies and expression signatures related to specific phenotypes are challenging problems in the context of gene expression data analysis. We present a method based on randomized embedding between euclidean subspaces to assess the stability of clusters characterized by low cardinality and very high dimensionality. Results with synthetic and gene expression data clustered with classical hierarchical clustering algorithms show the effectiveness of the proposed approach.

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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Theory & Algorithms
ID Code:2076
Deposited By:Giorgio Valentini
Deposited On:05 February 2006