Social web video clustering based on multi-view clustering via nonnegative matrix factorization

Abstract

Social web videos are rich data sources containing valuable information, which have a great potential to improve the performance of social web video clustering. Social web video data usually present a characteristic of multiple views. Multi-view clustering provides a useful way to generate clusters from multi-view data. Previous studies have applied different single-view data to do social web video clustering and classification; however, multi-view data has not been a factor considered in these methods. Therefore, in this paper, we propose a framework based on a novel online multi-view clustering algorithm (called SOMVCS) to cluster social web videos with large-scale possibly incomplete views into meaningful clusters. SOMVCS learns the latent feature matrices from all the views and then drives them towards a common consensus matrix based on nonnegative matrix factorization (NMF). Particularly, we incorporate graph regularization to preserve local structure information in the model. The experimental results show that online multi-view clustering via NMF is a preferable method for social web video clustering. Moreover, we find that using multi-view data with feature types from different feature families to do social web video clustering outperforms that using data with only the feature type from a single family.