Information Theoretic Clustering

By Erhan Gokcay, José Carlos Príncipe. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002

331 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/f7065b6edb59fb19d987c5d791e73514528ce0ca

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Citations in the corpus

Universal Estimation of Information Measures for Analog Sources (2009, methodology): “In [3, 94, 123], an information theoretic criterion is developed based on Rényi differential entropy to optimize the clustering results.”
Finding the Number of Clusters in a Dataset (2003, background): “Of the recently suggested clustering algorithms, perhaps the one making the most use of information-theoretic ideas is that of Gokcay and Principe (2002).”
Combining multiple clusterings using evidence accumulation (2005, methodology): “Nonparametric density-based clustering methods attempt to identify high density clusters separated by low density regions [5], [32], [33].”
Received-Signal-Strength-Based Indoor Positioning Using Compressive Sensing (2012, methodology): “The affinity propagation algorithm [24] is used to generate the clusters, as it does not require initialization of exemplars in the traditional K-means clustering algorithm [25].”
Kernel-Based Positioning in Wireless Local Area Networks (2007, methodology): “To produce a minimum distance of zero, dIðRipk ; R j pk Þ must be normalized as in [39].”
Robust data clustering (2003, background): “Hundreds of clustering algorithms exist [16, 6, 19, 9, 11, 14, 3, 18, 1, 12], yet it is difficult to find a single clustering algorithm that can handle all types of cluster shapes and sizes.”
A Kernel Method for the Two-Sample-Problem (2006, methodology): “(2008) build on the work of Gretton et al. (2007a), considering a homogeneity statistic arising from the kernel Fisher discriminant, rather than the difference of RKHS means; and Nguyen et al.”
Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images (2005, methodology): “Recently, Grau et al. [30] proposed an improved watershed method using prior information and they compare their approach for WM and GM segmentation with the methods of Van Leemput et al. [24] and Zeng et al. [31].”
A Kernel Two-Sample Test (2012, methodology): “For = 1 the distanceDr(p,q) is known as the Ĺevy distance (Feller, 1971), and forr = 2 we encounter a distance measure derived from the Renyi entropy (Gokcay and Principe, 2002).”