Abstract:An automatic neighborhood selection algorithm for manifold learning is proposed. It is suitable for all the manifold learning algorithms which need select the neighbors to get locally linear information. Through the algorithm, the proper neighborhood size of a dataset can be determined even under different data density and curvature. By adopting this method, the locally multidimensional scaling can reduce the dimensionality of data based on the suitable neighborhood, and the low-dimensional representations of the data can be get through global alignment. The experiment shows the algorithm can recover the sophisticated geometry structure of the data sets.
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