An entropic estimator for structure discovery
Proceedings of the 1998 conference on Advances in neural information processing systems II
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Information Theoretic Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Time Series Abstraction Methods - A Survey
Informatik bewegt: Informatik 2002 - 32. Jahrestagung der Gesellschaft für Informatik e.v. (GI)
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Fully Unsupervised Fuzzy Clustering with Entropy Criterion
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
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This paper presents an HMM based hierarchical clustering method, aiming at the extraction of typical temporal sequences, and outliers are discarded in the process of clustering. After the Baum-Welch training step of HMM, TWM (Transition Weighted Matrix) is used as the features of sample sequences, thus the original clustering problem is converted to a relatively easy problem of points clustering in a high dimensional space. By using hierarchical clustering and NCut (Normalized Cut) method, the unsteadiness in separation is efficiently prevented and the time consuming is relatively small. The method is used in unsupervised learning of typical hand gestures and facial expressions.