Abstract
In this work we propose a dictionary learning based clustering approach. We regularize dictionary learning with a clustering loss; in particular, we have used sparse subspace clustering and K-means clustering. The basic idea is to use the coefficients from dictionary learning as inputs for clustering. Comparison with state-of-the-art deep learning based techniques shows that our proposed method improves upon them.
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References
Ramirez, I., Sprechmann, P., Sapiro, G.: Classification and clustering via dictionary learning with structured incoherence and shared features. In: IEEE CVPR, pp. 3501–3508 (2010)
Sprechmann, P., Sapiro, G.: Dictionary learning and sparse coding for unsupervised clustering. In: IEEE ICASSP, pp. 2042–2045 (2010)
Xu, W., Liu, X., Gong, Y.: Document clustering based on non-negative matrix factorization. In: ACM SIGIR, pp. 267–273 (2003)
Ding, C., He, X., Simon, H.D.: On the equivalence of nonnegative matrix factorization and spectral clustering. In: SIAM SDM, pp. 606–610 (2005)
Li, T., Ding, C.: The relationships among various nonnegative matrix factorization methods for clustering. In: IEEE ICDM, pp. 362–371 (2006)
Tian, F., Gao, B., Cui, Q., Chen, E., Liu, T.Y.: Learning deep representations for graph clustering. In: AAAI, pp. 1293–1299 (2014)
Peng, X., Xiao, S., Feng, J., Yau, W.Y., Yi, Z.: Deep sub-space clustering with sparsity prior. In: IJCAI, pp. 1925–1931 (2016)
Xie, J., Girshick, R., Farhadi, A.: Unsupervised deep embedding for clustering analysis. In: ICML, pp. 478–487 (2016)
Yang, B., Fu, X., Sidiropoulos, N.D., Hong, M.: Towards k-means-friendly spaces: Simultaneous deep learning and clustering. In: ICML, pp. 3861–3870 (2017)
Fard, M.M., Thonet, T., Gaussier, E.: Deep k-means: Jointly clustering with k-means and learning representations. Pattern Recogn. Lett. 138, 185–192 (2020)
Guo, X., Liu, X., Zhu, E., Yin, J.: Deep clustering with convolutional autoencoders. In: ICONIP, pp. 373–382 (2017)
Yang, X., Deng, C., Zheng, F., Yan, J., Liu, W.: Deep spectral clustering using dual autoencoder network. In: IEEE CVPR, pp. 4061–4070 (2019)
Trigeorgis, G., Bousmalis, K., Zafeiriou, S., Schuller, B.W.: A deep matrix factorization method for learning attribute representations. IEEE Trans. Pattern Anal. Mach. Intell. 39(3), 417–429 (2017)
Ravishankar, S., Bresler, Y.: Learning sparsifying transforms. IEEE Trans. Signal Process. 61(5), 1072–1086 (2013)
Maggu, J., Majumdar, A., Chouzenoux, E.: Transformed Subspace Clustering. IEEE Trans. Knowl. Data Eng. 33, 1796–1801 (2020)
Maggu, J., Majumdar, A., Chouzenoux, E., Chierchia, G.: Deeply transformed subspace clustering. Signal Process. 174, 107628 (2020)
Bauckhage, C.: K-means clustering is matrix factorization (2015). arXiv preprint arXiv:1512.07548,
Elhamifar, E., Vidal, R.: Sparse Subspace clustering: algorithm, theory, and applications. IEEE Trans. Pattern Anal. Mach. Intell. 35(11), 2765–2781 (2013)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)
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This work is supported by Infosys Center for Artificial Intelligence at IIIT Delhi.
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Goel, A., Majumdar, A. (2021). Clustering Friendly Dictionary Learning. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13108. Springer, Cham. https://doi.org/10.1007/978-3-030-92185-9_45
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DOI: https://doi.org/10.1007/978-3-030-92185-9_45
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