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2006

Robust locally linear embedding

8 years 5 months ago
Robust locally linear embedding
In the past few years, some nonlinear dimensionality reduction (NLDR) or nonlinear manifold learning methods have aroused a great deal of interest in the machine learning community. These methods are promising in that they can automatically discover the low-dimensional nonlinear manifold in a high-dimensional data space and then embed the data points into a low-dimensional embedding space, using tractable linear algebraic techniques that are easy to implement and are not prone to local minima. Despite their appealing properties, these NLDR methods are not robust against outliers in the data, yet so far very little has been done to address the robustness problem. In this paper, we address this problem in the context of an NLDR method called locally linear embedding (LLE). Based on robust estimation techniques, we propose an approach to make LLE more robust. We refer to this approach as robust locally linear embedding (RLLE). We also present several specific methods for realizing this g...
Hong Chang, Dit-Yan Yeung
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where PR
Authors Hong Chang, Dit-Yan Yeung
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