A semi-supervised multiview spectral clustering algorithm based on distance metric learning

作者：Yang, Jinhong; Deng, Tingquan

来源：Journal of Sichuan University(Engineering Science Edition), 2016, 48(1): 146-151.

DOI：10.15961/j.jsuese.2016.01.022

• Distance Metric Learning
• Multi-view clustering
• Optimization problems
• Quality of clustering
• Semi-supervised Clustering
• Similarity metrics
• Spectral clustering
• Spectral clustering algorithms

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.

• 出版日期2016