Spectral clustering algorithm based on the spectrum based on the theory, as compared with traditional clustering algorithm, which has the advantage of clustering and convergence in the sample space to the global optimal solution of arbitrary shape. Firstly, according to the given sample data set defines a description paired data points of similarity affinity matrix, and compute eigenvalues and eigenvectors, and then the appropriate feature vector clustering of different data points. Spectral clustering algorithm was originally used in computer vision, VLS I design and other fields, only recently began to be used in machine learning, and quickly became the focus of international research on the field of machine learning.

pujulei.txt

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