Description: 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
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LEM\1.mat
...\2a.mat
...\2b.mat
...\2c.mat
...\2d.mat
...\4a.mat
...\Jain.mat
...\k_means.m
...\LE.m
...\LEM.jpg
...\LEM2_para15_4_2jpg.jpg
...\LEM_test1.m
...\R15.mat
LEM
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