Entropy-Rate Clustering: Cluster Analysis via Maximizing a Submodular Function Subject to a Matroid Constraint

We propose a new objective function for clustering. This objective function consists of two components: the entropy rate of a random walk on a graph and a balancing term. The entropy rate favors formation of compact and homogeneous clusters, while the balancing function encourages clusters with simi...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence Jg. 36; H. 1; S. 99 - 112
Hauptverfasser:	Ming-Yu Liu, Tuzel, Oncel, Ramalingam, Srikumar, Chellappa, Rama
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Los Alamitos, CA IEEE 01.01.2014 IEEE Computer Society The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithm design and analysis Algorithms Applied sciences Artificial intelligence Clustering Clustering algorithms Combinatorics Combinatorics. Ordered structures Computer science; control theory; systems Data processing. List processing. Character string processing discrete optimization Entropy Exact sciences and technology Graph theory Heuristic Image segmentation Information retrieval. Graph information theory Linear programming Mathematics Memory organisation. Data processing Pattern recognition. Digital image processing. Computational geometry Sciences and techniques of general use Software submodular function superpixel segmentation Theoretical computing Topology Uncertainty Cluster analysis Data analysis Graph construction Combinatorial problem Random walk Matroid Group size Cluster Entropy Graph theory Topology Clustering Random graph superpixel segmentation Submodular function Image segmentation Vector space Greedy algorithm discrete optimization Discrete programming Metric Objective function Linear space Information theory
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online-Zugang:	Volltext
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