Description: Spectral clustering can distinguish arbitrary sample space and converge to the global optimal solution, the basic idea is similar to matrix of eigenvectors obtained after decomposition of clustering using the sample data, procedures for the comparison of several different clustering algorithms, including Q matrix clustering, kmeans clustering, the first feature clustering, second generalized characteristic component clustering, public data generation and neighbor matrix generation
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Spectral Clustering\Scott_Longuet_Higgins.m
Spectral Clustering\Shi_Malik.m
Spectral Clustering\CalculateAffinity.m
Spectral Clustering\GenerateData.m
Spectral Clustering\Jordan_Weiss.m
Spectral Clustering\Perona_Freeman.m
license.txt
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