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Non-negative matrix factorisation based on fuzzy K nearest neighbour graph and its applications

Non-negative matrix factorisation based on fuzzy K nearest neighbour graph and its applications

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Non-negative matrix factorisation (NMF) has been widely used in pattern recognition problems. For the tasks of classification, however, most of the existing variants of NMF ignore both the discriminative information and the local geometry of data into the factorisation. The actual conditions of the problems will be affected by the change of the environmental factors to affect the recognition accuracy. In order to overcome these drawbacks, the authors regularised NMF by intra-class and inter-class fuzzy K nearest neighbour graphs, leading to NMF-FK-NN in this study. By introducing two novel fuzzy K nearest neighbour graphs, NMF-FK-NN can contract the intra-class neighbourhoods and expand the inter-class neighbourhoods in the decomposition. This method not only exploits the discriminative information and uses the geometric structure in the data effectively, but also reduces the influence of the external factors to improve recognition effect. In the factorisation, the authors minimised the approximation error whilst contracting intra-class fuzzy neighbourhoods and expanding inter-class fuzzy neighbourhoods. The authors develop simple multiplicative updates for NMF-FK-NN and present monotonic convergence results. Experiments of the text clustering on the CLUTO toolkit and face recognition on ORL and YALE datasets show the effectiveness of our proposed method.

References

    1. 1)
      • 1. Kirby, M., Sirovich, L.: ‘Application of the Karhunen-Loeve procedure for the characterization of human faces’, IEEE Trans. PAMI, 1990, 12, (1), pp. 103108 (doi: 10.1109/34.41390).
    2. 2)
      • 2. Chengjun, L., Wechsler, H.: ‘Independent component analysis of Gabor features for face recognition’, IEEE Trans. Neural Netw., 2003, 14, (4), pp. 919928 (doi: 10.1109/TNN.2003.813829).
    3. 3)
      • 3. Lee, D.D., Seung, H.S.: ‘Learning the parts of objects by nonnegative matrix factorization’, Nature, 1999, 401, (21), pp. 788791.
    4. 4)
      • 4. Cai, D., He, X., Wu, X., Han, J.: ‘Non-negative matrix factorization on manifold’. Proc. IEEE Int. Conf. on Data Mining, 2008, pp. 6372.
    5. 5)
      • 5. Belkin, M., Niyogi, P.: ‘Laplacian eigenmaps and spectral techniques for embedding and clustering’, Adv. Neural Inf. Process. Syst., 2002, 14, pp. 585591.
    6. 6)
      • 6. He, X., Niyogi, P.: ‘Locality preserving projections’, Adv. Neural Inf. Process. Syst., 2003, 15, pp. 18.
    7. 7)
      • 7. Guan, N., Tao, D., Luo, Z., Yuan, B.: ‘Manifold regularized discriminative nonnegative matrix factorization with fast gradient descent’, IEEE Trans. Image Process., 2011, 20, (7), pp. 20302048 (doi: 10.1109/TIP.2011.2105496).
    8. 8)
      • 8. Li, S.Z., Hou, X., Zhang, H., Cheng, Q.: ‘Learning spatially localized, parts-based representation’. Proc. IEEE Int. Conf. on Comput. Vis. Pattern Recognition, 2001, pp. 207212.
    9. 9)
      • 9. Hoyer, P.: ‘Non-negative matrix factorization with sparseness constraints’, J. Mach. Learn. Res., 2004, 5, pp. 14571469.
    10. 10)
      • 10. Wang, Y., Jia, Y., Hu, C., Turk, M.: ‘Fisher non-negative matrix factorization for learning local features’. Proc. Asian Conf. on Computer Vision (ACCV), Jeju Island, Korea, 2004.
    11. 11)
      • 11. Zafeiriou, S., Tefas, A., Buciu, I., Pitas, I.: ‘Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification’, IEEE Trans. Neural Netw., 2006, 17, (3), pp. 683695 (doi: 10.1109/TNN.2006.873291).
    12. 12)
      • 12. An, S., Yoo, J., Choi, S.: ‘Manifold-respecting discriminant nonnegative matrix factorization’, Pattern Recognit. Lett., 2011, 32, pp. 832837 (doi: 10.1016/j.patrec.2011.01.012).
    13. 13)
      • 13. Kwak, K.C., Pedrycz, W.: ‘Face recognition using a fuzzy Fisherface classifier’, Pattern Recognit., 2005, 38, (10), pp. 17171732 (doi: 10.1016/j.patcog.2005.01.018).
    14. 14)
      • 14. Keller, J.M., Gray, M.R., Givens, J.A.: ‘A fuzzy k-nearest neighbor algorithm’, IEEE Trans. Syst., Man Cybernet., 1985, 15, (4), pp. 580585.
    15. 15)
      • 15. Wan, M., Yang, G., Lai, Z., Jin, Z.: ‘Feature extraction based on fuzzy local discriminant embedding with applications to face recognition’, IET Comput. Vis., 2011, 5, (5), pp. 301308 (doi: 10.1049/iet-cvi.2011.0028).
    16. 16)
      • 16. Chen, H.T., Chang, H.W., Liu, T.L.: ‘Local discriminant embedding and its variants’. Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, 2005, pp. 846853.
    17. 17)
      • 17. Lee, D.D., Seung, H.S.: ‘Algorithms for nonnegative matrix factorization’, Adv. Neural Inf. Process. Syst. (NIPS), 2000, pp. 556562.
    18. 18)
      • 18. Lovasz, L.: ‘Matching Theory (North-Holland Mathematics Studies)’ (Elsevier Science Ltd., 1986).
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