Advertisement

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

  • Vinath Mekthanavanh
  • Tianrui LiEmail author
  • Hua Meng
  • Yan Yang
  • Jie Hu
Original Article
  • 15 Downloads

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.

Keywords

Multi-view clustering Nonnegative matrix factorization (NMF) Social web videos mining 

Notes

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).

References

  1. 1.
    Zhang DQ, Lin CY, Chang SF, Smith JR (2004) Semantic video clustering across sources using bipartite spectral clustering. In: Proceedings of international conference on multimedia and expo (ICME), vol 1. IEEE, pp 117–120Google Scholar
  2. 2.
    Guil N, González-Linares JM, Cózar JR, Zapata EL (2007) A clustering technique for video copy detection. In: Proceedings of the 3rd Iberian conference on pattern recognition and image analysis, Part I, pp 451–458Google Scholar
  3. 3.
    Hindle A, Shao J, Lin D, Lu J, Zhang R (2011) Clustering web video search results based on integration of multiple features. World Wide Web 14(1):53–73CrossRefGoogle Scholar
  4. 4.
    Gargi U, Lu W, Mirrokni VS, Yoon S (2011) Large-scale community detection on YouTube for topic discovery and exploration. In: Proceedings of 5th international conference on weblogs and social media. The AAAI Press, Palo Alto, pp 486–489Google Scholar
  5. 5.
    Kamie M, Hashimoto T, Kitagawa H (2012) Effective web video clustering using playlist information. In: Proceedings of the 27th annual ACM symposium on applied computing. ACM, New York, pp 949–956Google Scholar
  6. 6.
    Mahmood A, Li T, Yang Y, Wang H, Afzal M (2015) Semi-supervised evolutionary ensembles for web video categorization. Knowl Based Syst 76:53–66CrossRefGoogle Scholar
  7. 7.
    Mekthanavanh V, Li T (2016) Social web videos clustering based on ensemble technique. In: Proceedings of international joint conference on rough sets. Springer, Berlin, pp 449–458Google Scholar
  8. 8.
    Wang H, Fan W, Yu PS, Han J (2003) Mining concept drifting data streams using ensemble classifiers. In: Proceedings of the ninth ACM SIGKDD international conference on knowledge discovery and data mining, pp 226–235Google Scholar
  9. 9.
    Trivedi A, Rai P, Daume H, DuVall SL (2010) Multiview clustering with incomplete views. In: NIPS workshop on machine learning for social computing, WhistlerGoogle Scholar
  10. 10.
    Li SY, Jiang Y, Zhou ZH (2014) Partial multi-view clustering. In: Proceedings of the 28th AAAI conference on artificial intelligence, pp 1968–1974Google Scholar
  11. 11.
    Shao W, He L, Philip SY (2015) Multiple incomplete views clustering via weighted nonnegative matrix factorization with L2;1 regularization. In: Proceedings of the joint European conference on machine learning and knowledge discovery in databases. Springer, Berlin, pp 318–334Google Scholar
  12. 12.
    Cichocki A, Zdunek R, Phan AH, Amari S (2009) Nonnegative matrix and tensor factorizations: applications to exploratory multi-way data analysis and blind source separation. Wiley, New YorkCrossRefGoogle Scholar
  13. 13.
    Berry MW, Browne M, Langville AN, Pauca VP, Plemmons RJ (2007) Algorithms and applications for approximate nonnegative matrix factorization. Comput Stat Data Anal 52(1):155–173MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Ding C, Li T, Peng W, Park H (2006) Orthogonal nonnegative matrix t-factorizations for clustering. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 126–135Google Scholar
  15. 15.
    Zhou G, Zhao J, Zeng D (2014) Sentiment classification with graph co-regularization. In: Proceedings of COLING 2014, the 25th international conference on computational linguistics, pp 1331–1340Google Scholar
  16. 16.
    Zhou G, He T, Wu W, Hu XT (2015) Linking heterogeneous input features with pivots for domain adaptation. In: Proceedings of the 24th international joint conference on artificial intelligence, pp 1419–1425Google Scholar
  17. 17.
    Liu J, Wang C, Gao J, Han J (2013) Multi-view clustering via joint nonnegative matrix factorization. In: Proceedings of the 2013 SIAM international conference on data mining, pp 252–260Google Scholar
  18. 18.
    Wang Z, Kong X, Fu H, Li M, Zhang Y (2015) Feature extraction via multi-view non-negative matrix factorization with local graph regularization. In: 2015 IEEE international conference on image processing (ICIP), pp 3500–3504Google Scholar
  19. 19.
    Cai D, He X, Han J, Huang TS (2011) Graph regularized nonnegative matrix factorization for data representation. IEEE Trans Pattern Anal Mach Intell 33(8):1548–1560CrossRefGoogle Scholar
  20. 20.
    Wang F, Tan C, Li P, Konig AC (2011) Efficient document clustering via online nonnegative matrix factorizations. In: Proceedings of the 2011 SIAM international conference on data mining. Society for industrial and applied mathematics, pp 908–919Google Scholar
  21. 21.
    Guan N, Tao D, Luo Z, Yuan B (2012) Online nonnegative matrix factorization with robust stochastic approximation. IEEE Trans Neural Netw Learn Syst 23(7):1087–1099CrossRefGoogle Scholar
  22. 22.
    Shao W, He L, Lu CT, Wei X, Philip SY (2016) Online unsupervised multi-view features selection. In: Proceedings of the IEEE 16th international conference on data mining, pp 1203–1208Google Scholar
  23. 23.
    Bickel S, Scheffer T (2004) Multi-view clustering. In: Proceedings of international conference on data mining (ICDM), vol 4, pp 19–26Google Scholar
  24. 24.
    Sun S (2013) A survey of multi-view machine learning. Neural Comput Appl 23(7–8):2031–2038CrossRefGoogle Scholar
  25. 25.
    Akata Z, Bauckhage C, Thurau C (2011) Non-negative matrix factorization in multimodality data for segmentation and label prediction. In: Proceedings of the 16th computer vision winter workshop, pp 1–8Google Scholar
  26. 26.
    Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 40(6755):788–791CrossRefzbMATHGoogle Scholar
  27. 27.
    Greene D, Cunningham P (2009) A matrix factorization approach for integrating multiple data views. In: Proceedings of joint European conference on machine learning and knowledge discovery in databases. Springer, Berlin, Heidelberg, pp 423–438CrossRefGoogle Scholar
  28. 28.
    Zhang X, Zong L, Liu X, Yu H (2015) Constrained NMF-based multi-view clustering on unmapped data. In: Proceedings of the 29th AAAI conference on artificial intelligence, pp 3174–3180Google Scholar
  29. 29.
    Kalayeh MM, Idrees H, Shah M (2014) NMF-KNN: image annotation using weighted multi-view nonnegative matrix factorization. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 184–191Google Scholar
  30. 30.
    Shao W, He L, Lu CT, Philip SY (2016) Online multiview clustering with incomplete views. In: Proceedings of the IEEE international conference on big data, pp 1012–1017Google Scholar
  31. 31.
    Madani O, Georg M, Ross DA (2012) On using nearly independent feature families for high precision and confidence. In: Proceedings of Asian conference on machine learning, pp 269–284Google Scholar
  32. 32.
    Cai D, He X, Han J (2005) Document clustering using locality preserving indexing. IEEE Trans Knowl Data Eng 17(12):1624–1637CrossRefGoogle Scholar
  33. 33.
    Xu W, Liu X, Gong Y (2003) Document clustering based on non-negative matrix factorization. In: Proceedings of the 26th annual international ACM SIGIR conference on research and development in information retrieval, pp 267–273Google Scholar
  34. 34.
    Lovàsz L, Plummer M (1986) Matching theory. Mathematics studies, vol 121. North Holland, AmsterdamzbMATHGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Vinath Mekthanavanh
    • 1
  • Tianrui Li
    • 1
    Email author
  • Hua Meng
    • 1
  • Yan Yang
    • 1
  • Jie Hu
    • 1
  1. 1.School of Information Science and TechnologySouthwest Jiaotong UniversityChengduChina

Personalised recommendations