Capped ℓ_p-Norm graph embedding for photo clustering

Photos are a predominant source of information on a global scale. Cluster analysis of photos can be applied to situation recognition and understanding cultural dynamics. Graphbased learning provides a current approach for modeling data in clustering problems. However, the performance of this framework depends heavily on initial graph construction by input data. Data outliers degrade graph quality, leading to poor clustering results. We designed a new capped ℓ_p-norm graph-based model to reduce the impact of outliers. This is accomplished by allowing the data graph to self adjust as part of the graph embedding. Furthermore, we derive an iterative algorithm to solve the objective function optimization problem. Experiments on four real-world benchmark data sets and Yahoo Flickr Creative Commons data set show the effectiveness of this new graph-based capped ℓ_p-norm clustering method.