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

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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.

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Notes

  1. http://archive.ics.uci.edu/ml/datasets/youtube+multiview+video+games+dataset.

  2. http://www.youtube.com.

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Acknowledgements

This work was supported by the National Science Foundation of China (nos. 61573292, 61572407, 61603313) and the Fundamental Research Funds for the Central Universities (No. 2682017CX097).

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Authors and Affiliations

  1. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 611756, China

    Vinath Mekthanavanh, Tianrui Li, Hua Meng, Yan Yang & Jie Hu

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  1. Vinath Mekthanavanh
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  2. Tianrui Li
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  3. Hua Meng
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  4. Yan Yang
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  5. Jie Hu
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Correspondence to Tianrui Li.

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Mekthanavanh, V., Li, T., Meng, H. et al. Social web video clustering based on multi-view clustering via nonnegative matrix factorization. Int. J. Mach. Learn. & Cyber. 10, 2779–2790 (2019). https://doi.org/10.1007/s13042-018-00902-5

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