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The local linear embedding (LLE) and Laplacian eigenmaps are two of the most popular manifold learning approaches since they can perform much faster than the other approaches. However, the LLE is sensitive to local structure and noises and the Laplacian eigenmaps, though more robust, cannot model and retain local linear structures. In this paper, a direct extension of LLE, called local linear Laplacian...
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