LEM (Laplace feature mapping) algorithm, Laplace feature mapping is based on a local neighborhood, holding manifold learning method local structure. LEM through a non-weighted graph depicts flow to form a neighbor relationship between data points, the graph' s vertex to the original data points, corresponding points edge neighbor relationship graph between the edge weights corresponding to the degree of similarity between neighboring points ( may be some distance), LEM keep neighbor relationship diagram between the data points in a low dimensional embedding space, and then strike embedding coordinates. Popular to say, LEM believes in high-dimensional data space be closer point in the low-dimensional embedding space should be closer

LEM\1.mat
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LEM

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