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Weighted NMF-Based Multiple Sparse Views Clustering for Web Items

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Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 10235))

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Abstract

Many web items contain different types of information resources such as user profile, comments, users preference and so on. All these aspects can be seen as different views of real-world datasets and often admit same underlying clustering of the data. However, each view of dataset forming a huge sparse matrix results in the non-robust characteristic during matrix decomposition process, and further influences the accuracy of clustering results. In this paper, we attempt to use rating value given by the users as latent semantic information to handle those features that are unobserved in each data point so as to resolve the sparseness problem in all views matrices. To combine multiple views in our constructed corpusDoucom, we present WScoNMF (Weighted similarity co-regularized Non-negative Matrix Factorization), which provides an efficient weighted matrix factorization framework to further explore the sparseness problem in semantic space of data. The overall objective function is to minimize the loss function of weighted NMF under the\(l _{2,1}\)-norm and the co-regularized constraint under theF-norm. Experimental results on all datasets demonstrate the effectiveness of the proposed method.

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Notes

  1. 1.

    In this paper, we use ‘data point’ and ‘item’ exchangeable.

  2. 2.

    \(\odot \) in matrix denote element-wise multiplication.

  3. 3.
  4. 4.
  5. 5.
  6. 6.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61472241) and the National High Technology Research and Development Program of China (No. 2015AA015303).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Shanghai Jiao Tong University, Shanghai, China

    Xiaolong Gong, Fuwei Wang & Linpeng Huang

Authors
  1. Xiaolong Gong

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  2. Fuwei Wang

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  3. Linpeng Huang

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Corresponding author

Correspondence toLinpeng Huang.

Editor information

Editors and Affiliations

  1. Kangwon National University, Chuncheon, Korea (Republic of)

    Jinho Kim

  2. Seoul National University, Seoul, Korea (Republic of)

    Kyuseok Shim

  3. University of Technology Sydney, Sydney, New South Wales, Australia

    Longbing Cao

  4. KAIST, Daejeon, Korea (Republic of)

    Jae-Gil Lee

  5. University of New South Wales, Sydney, New South Wales, Australia

    Xuemin Lin

  6. Kangwon National University, Chuncheon, Korea (Republic of)

    Yang-Sae Moon

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Gong, X., Wang, F., Huang, L. (2017). Weighted NMF-Based Multiple Sparse Views Clustering for Web Items. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10235. Springer, Cham. https://doi.org/10.1007/978-3-319-57529-2_33

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