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13 January 2012 Locally linear embedding based on local correlation
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Abstract
The task of nonlinear dimensionality reduction is to find meaningful low-dimensional structures hidden in high dimensional data. In this paper, an unsupervised algorithm for nonlinear dimensionality reduction called locally linear embedding based on local correlation (LC-LLE) is presented. The LC-LLE algorithm is motivated by locally linear embedding (LLE) algorithm and correlation coefficient which usually gives the correlation between two random vectors. It is a major advantage of the LC-LLE to optimize the process of dimensionality reduction by giving more reasonable neighbor searching. Simulation studies demonstrate that the LC-LLE can give better results in dimension reduction than LLE. Experiments on face images data sets have shown the potential of LC-LLE in practical problem.
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Jing Chen and Yang Liu "Locally linear embedding based on local correlation", Proc. SPIE 8350, Fourth International Conference on Machine Vision (ICMV 2011): Computer Vision and Image Analysis; Pattern Recognition and Basic Technologies, 83502M (13 January 2012); https://doi.org/10.1117/12.920235
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