论文信息 - Active learning for hierarchical pairwise data clustering

Active learning for hierarchical pairwise data clustering

Pairwise data clustering is a well founded grouping technique based on relational data of objects which has a widespread application domain. However, its applicability suffers from the disadvantageous fact that N objects give rise to N(N-1)/2 relations. To cure this unfavorable scaling, techniques to sparsely sample the relations have been developed. Yet a randomly chosen subset of the data might not grasp the structure of the complete data set. To overcome this deficit, we use active learning methods from the field of statistical decision theory. Extending existing approaches we present an algorithm for actively learning hierarchical group structures based on mean field annealing optimization.

J. Buhmann | Thomas Zöller

[1] H. Raiffa,et al. Introduction to Statistical Decision Theory , 1996 .

[2] Joachim M. Buhmann,et al. Active Data Clustering , 1997, NIPS.

[3] Anders Krogh,et al. Neural Network Ensembles, Cross Validation, and Active Learning , 1994, NIPS.

[4] Joachim M. Buhmann,et al. A theory of proximity based clustering: structure detection by optimization , 2000, Pattern Recognit..

[5] Constantino Tsallis,et al. Optimization by Simulated Annealing: Recent Progress , 1995 .

[6] Kenneth Rose,et al. Hierarchical, Unsupervised Learning with Growing via Phase Transitions , 1996, Neural Computation.

[7] David A. Cohn,et al. Active Learning with Statistical Models , 1996, NIPS.

[8] M. Hasenjäger,et al. Active Learning in Self-Organizing Maps , 1999 .

[9] David J. C. MacKay,et al. Information-Based Objective Functions for Active Data Selection , 1992, Neural Computation.

[10] Geoffrey C. Fox,et al. A deterministic annealing approach to clustering , 1990, Pattern Recognit. Lett..