Abstract
Subspace clustering finds clusters in subspaces of the data instead of the entire data space to deal with high-dimensional data. Most existing subspace clustering algorithms lean on just one single objective function. Single objective function is often biased. On the other hand, most existing subspace clustering algorithms are based on wrapper approach, which brings a negative effect on the quality of subspace clustering. This paper presents a soft subspace clustering algorithm based on multi-objective evolutionary algorithm and reliability measure, called R-MOSSC. Comparing with optimization of a scalar function combining multiple objectives, it does not need to determine weight hyperparameters, and offers a deep insight into the problem by obtaining a set of solutions. Further, reliability-based dimension weight matrix from filter approach is used to enhance the performance of subspace clustering. Simulation results show that R-MOSSC is better than existing algorithms.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (60805026, 61070076, 61272065), Natural Science Foundation of Guangdong Province (S2011020001182), Research Foundation of Science and Technology Plan Project in Guangdong Province and Guangzhou City (2010A040303004, 2011B040200007, 2011A091000026, 11A12050914, 11A31090341, 2011Y5-00004), Research Foundation of NSFC-Guangdong Key project (U0935002), and the Zhujiang New Star of Science and Technology in Guangzhou City (2011J2200093).
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Bi, Z., Wang, J., Yin, J. (2013). A Soft Subspace Clustering Algorithm Based on Multi-Objective Optimization and Reliability Measure. In: Yin, Z., Pan, L., Fang, X. (eds) Proceedings of The Eighth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 2013. Advances in Intelligent Systems and Computing, vol 212. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37502-6_30
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DOI: https://doi.org/10.1007/978-3-642-37502-6_30
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