Computational Intelligence and Neuroscience / 2017 / Article / Alg 1

Research Article

Fast Constrained Spectral Clustering and Cluster Ensemble with Random Projection

Algorithm 1

Fast constrained spectral clustering.
Input: data set , the number of landmark points , constraint matrix , cluster number ,
      confidence parameter , sample rate ;
Output: the grouping result.
() Compute the sparse representation in Equation (5);
() Compute Laplacian and , where
   is the Laplacian matrix of , is the Laplacian matrix of ;
() Solve the first non-trivial generalized eigenvectors of Equation (12);
() Compute ;
() Embed into a -dimensional sphere using the embedding process in [17];
() Sample row vectors of randomly and run -means clustering on the sampled row vectors;
() Get the clustering result utilizing distances between centers of -means clustering and row vectors of .

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