2007 IEEE International Conference on Granular Computing (GRC 2007) Sparse Possibilistic Clustering with L1 Regularization San Jose, California November 02-November 04 ISBN: 0-7695-3032-X
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/GrC.2007.125
Possibilistic clustering is an efficient method to detect high density regions and more robust than fuzzy c-means. However, it is not `sparse', since a cluster center is ex- pressed as a linear combination of all data. In this paper, we propose a sparse possibilistic clustering method with l1 regularization to find compact clusters. Due to a non- negative constraints for a membership, the baseline con- stant is introduced into the regularizer. The effectiveness of the proposed method is shown in illustrative examples.
Citation:
Ryo Inokuchi, Sadaaki Miyamoto, "Sparse Possibilistic Clustering with L1 Regularization," grc, pp.442, 2007 IEEE International Conference on Granular Computing (GRC 2007), 2007 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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