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| Self-Regulation of Neighborhood Parameter for Locally Linear Embedding |
| HUI Kang-Hua1,2,XIAO Bai-Hua1,WANG Chun-Heng1 |
1.Key Laboratory of Complex Systems and Intelligence Science,Institute of Automation,Chinese Academy of Sciences,Beijing 100190
2.College of Computer Science and Technology,Civil Aviation University of China,Tianjin 300300 |
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Abstract The locally linear embedding (LLE) algorithm is considered as a powerful method for the problem of nonlinear dimensionality reduction. A method called self-regulated LLE is proposed. It finds the locally linear patch by analyzing the locally linear reconstruction errors of each sample in the dataset. Then, according to samples on the locally linear patch, it selects the appropriate neighborhood parameter for LLE. The experimental results show that LLE with self-regulation performs better than LLE based on different evaluation criteria and spends less time on several datasets.
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Received: 22 June 2009
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2010, Vol. 23 
