This paper deals with an acronym/definition extraction approach from textual data (corpora) and the disambiguation of these definitions (or expansions). Both steps of our global pr...
Most of the biclustering algorithms for gene expression data are based either on the Euclidean distance or correlation coefficient which capture only linear relationships. However...
In the past decade, information theory has been studied extensively in computational imaging. In particular, image matching by maximizing mutual information has been shown to yiel...
Igor Yanovsky, Paul M. Thompson, Stanley Osher, Al...
Quantization of continuous variables is important in data analysis, especially for some model classes such as Bayesian networks and decision trees, which use discrete variables. Of...
In this paper, a new algorithm for blind inversion of Wiener systems is presented. The algorithm is based on minimization of mutual information of the output samples. This minimiz...
We present feature transformations useful for exploratory data analysis or for pattern recognition. Transformations are learned from example data sets by maximizing the mutual inf...
Correlations between alleles after selection are an important source of information. Such correlations should be exploited for further search and thereby constitute the building bl...
Mutual information has become a popular similarity measure in multi-modality medical image registration since it was first applied to the problem in 1995. This paper describes a m...
We present an information theoretic approach for learning a linear dimension reduction transform for object classification. The theoretic guidance of the approach is that the trans...
Abstract. This paper presents a comparison between direct and recursive prediction strategies. In order to perform the input selection, an approach based on mutual information is u...
Yongnan Ji, Jin Hao, Nima Reyhani, Amaury Lendasse