Maximum Margin Clustering without Nonconvex Optimization: an Equivalent Transformation

DOI

10.2991/itms-15.2015.348How to use a DOI?

Keywords

Maximum margin clustering; Spectral clustering; Kernel machine

Abstract

On account of the promising performance in accuracy, maximum margin clustering (MMC) has attracted attentions from many research domains. MMC derived from the extension of support vector machine (SVM). But due to the undetermined labeling of samples in dataset, the original optimization is a nonconvex problem which is time-consuming to solve. Based on another high-quality nonlinear clustering technique—spectral clustering, this paper discusses an equivalent transformation of MMC into spectral clustering. By virtue of the establishment of equivalent relation between MMC and spectral clustering, we search for a simplified spectral clustering based method to solve the optimization problem of MMC efficiently, reducing its computational complexity. Experimental results on real world datasets show that the clustering results of MMC from the equivalent transformed spectral clustering method are better than any other baseline algorithms in comparison, and the reduced time consuming makes this advanced MMC more scalable.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Y. Kang
AU  - Z.Y. Liu
AU  - W. P. Wang
AU  - D. Meng
PY  - 2015/11
DA  - 2015/11
TI  - Maximum Margin Clustering without Nonconvex Optimization: an Equivalent Transformation
BT  - Proceedings of the 2015 International Conference on Industrial Technology and Management Science
PB  - Atlantis Press
SP  - 1425
EP  - 1428
SN  - 2352-538X
UR  - https://doi.org/10.2991/itms-15.2015.348
DO  - 10.2991/itms-15.2015.348
ID  - Kang2015/11
ER  -