In order to take the advantage of prior knowledge to improve clustering performance,based on distance metric learning (ML SMC),a semi supervised multi view spectral clustering algorithm was proposed.The prior knowledge was incorporated into clustering process by distance metric learning, which mapped data into a new space which subjects to prior knowledge.Each graph of views was constructed according to similarity metric, and then the problem of multi view clustering was formulated as an optimization problem of minmum normalized cut.Experiments showed that the quality of clustering results of ML SMC is superior to three classical multiview clustering algorithms and four semi supervised single view clustering algorithms,and the precision of ML SMC could be significantly improved by incorporating some prior knowledge.
