References
- A. Asuncion and D. Newman, UCI machine learning repository, 2007. Available at http://archive.ics.uci.edu/ml/index.php.
- J.C. Bezdek, R. Ehrlich, and W. Full, FCM: The fuzzy c-means clustering algorithm, Comput. Geosci. 10 (1984), pp. 191–203.
- C. Bouveyron and C. Brunet, Simultaneous model-based clustering and visualization in the fisher discriminative subspace, Stat. Comput. 22 (2012), pp. 301–324.
- M.M. Chiang and B. Mirkin, Intelligent choice of the number of clusters in K-means clustering: An experimental study with different cluster spreads, J. Classif. 27 (2010), pp. 3–40.
- C.L. Chowdhary and D.P. Acharjya, Breast Cancer Detection using Intuitionistic Fuzzy Histogram Hyperbolization and Possibilitic Fuzzy c-mean Clustering Algorithms with Texture Feature based Classification on Mammography Images, In Proceedings of the International Conference on Advances in Information Communication Technology & Computing (AICTC '16). Association for Computing Machinery, New York, NY, USA, Article 21, 1–6.
- C.L. Chowdhary and D.P. Acharjya, Clustering algorithm in possibilistic exponential fuzzy C-mean segmenting medical images, J. Biomimetics, Biomater. Biomed. Eng. 30 (2017), pp. 12–23.
- C.L. Chowdhary and D.P. Acharjya, Segmentation of mammograms using a novel intuitionistic possibilistic fuzzy c-mean clustering algorithm, in Nature inspired computing. Advances in intelligent systems and computing, B. Panigrahi, M. Hoda, V. Sharma, S. Goel, eds., vol 652. Springer, Singapore, 2018.
- C. Ding and T. Li, Adaptive Dimension Reduction using Discriminant Analysis and K-means Clustering, In Proceedings of the 24th international conference on Machine learning (ICML '07). Association for Computing Machinery, New York, NY, USA, 2007, pp. 521–528.
- E. Elhamifar and R. Vidal, Sparse subspace clustering: Algorithm, theory, and applications, IEEE Trans. Pattern Anal. Mach. Intell. 35 (2013), pp. 2765–2781.
- J.H. Friedman, Regularized discriminant analysis, J. Am. Stat. Assoc. 84 (1989), pp. 165–175.
- K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed., Academic Press, 1990.
- P.W. Holland and R.E. Welsch, Robust regression using iteratively reweighted least-squares, Commun. Stat. 6 (1977), pp. 813–827.
- C. Hou, F. Nie, D. Yi, and D. Tao, Discriminative embedded clustering: A framework for grouping high-dimensional data, IEEE Trans. Neural Netw. Learn. Syst. 26 (2015), pp. 1287–1299.
- G. Huang, T. Liu, Y. Yang, Z. Lin, S. Song, and C. Wu, Discriminative clustering via extreme learning machine, Neural Netw. 70 (2015), pp. 1–8.
- P.J. Huber, Robust Statistics, Wiley, New York, 1981.
- J.J. Hull, A database for handwritten text recognition research, IEEE Trans. Pattern Anal. Mach. Intell. 16 (2002), pp. 550–554.
- I.T. Jolliffe, Principal Component Analysis, 2nd ed., Springer, 2002.
- R. Krishnapuram and J.M. Keller, The possibilistic C-means algorithm: Insights and recommendations, IEEE Trans. Fuzzy Syst. 4 (1996), pp. 385–393.
- R. Krishnapuram and J.M. Keller, A possibilistic approach to clustering, IEEE Trans. Fuzzy Syst. 1 (2002), pp. 98–110.
- H. Li, T. Jiang, and K. Zhang, Efficient and robust feature extraction by maximum margin criterion, IEEE Trans. Neural Netw. 17 (2006), pp. 157–165.
- X. Li, H. Zhang, and R. Zhang, Adaptive graph auto-encoder for general data clustering, preprint (2020). Available at arXiv:2002.08648.
- A.Y. Ng, M.I. Jordan, and Y. Weiss, On Spectral Clustering: Analysis and an Algorithm, Proceedings of the NIPS, MIT Press, USA, 2002, pp. 849–856.
- C.C. Paige and M.A. Saunders, LSQR: An algorithm for sparse linear equations and sparse least squares, ACM Trans. Math. Softw. 8 (1982), pp. 43–71.
- N.R. Pal, K. Pal, J.M. Keller, and J.C. Bezdek, A possibilistic fuzzy c-means clustering algorithm, IEEE Trans. Fuzzy Syst. 13 (2005), pp. 517–530.
- X. Peng, J. Feng, S. Xiao, W.-Y. Yau, J.T. Zhou, and S. Yang, Structured autoencoders for subspace clustering, IEEE Trans. Image Process. 27 (2018), pp. 5076–5086.
- X. Peng, Z. Yu, Z. Yi, and H. Tang, Constructing the l2-graph for robust subspace learning and subspace clustering, IEEE Trans. Cybern. 47 (2017), pp. 1053–1066.
- X. Peng, H. Zhu, J. Feng, C. Shen, H. Zhang, and J.T. Zhou, Deep clustering with sample-assignment invariance prior, IEEE Trans. Neural Netw. Learn. Syst. 99 (2019), pp. 1–12.
- Y. Peng, W.L. Zheng, and B.L. Lu, An unsupervised discriminative extreme learning machine and its applications to data clustering, Neurocomputing 174 (2016), pp. 250–264.
- L. Sun, B. Ceran, and J. Ye, A scalable two-stage approach for a class of dimensionality reduction techniques, International Conference on Knowledge Discovery and Data Mining, ACM, New York, NY, 2010, pp. 313–322.
- F. Torre and T. Kanade, Discriminative Cluster Analysis, in Proceedings of the 23rd international conference on Machine learning (ICML '06). Association for Computing Machinery, New York, NY, USA, 2006, pp. 241–248.
- K. Treerattanapitak and C. Jaruskulchai, Membership Enhancement with Exponential Fuzzy Clustering for Collaborative Filtering, Neural Information Processing. Theory and Algorithms – 17th International Conference, ICONIP 2010, Sydney, Australia, November 22–25, 2010, Proceedings, Part I, Springer Berlin Heidelberg, 2010.
- K. Treerattanapitak and C. Jaruskulchai, Exponential fuzzy C-means for collaborative filtering, J. Comput. Sci. Technol. 27 (2012), pp. 567–576.
- K.L. Wu and M.S. Yang, Alternative c-means clustering algorithms, Pattern Recognit. 35 (2002), pp. 2267–2278.
- Q. Xu, C. Ding, J. Liu, and B. Luo, PCA-guided search for K-means, Pattern Recognit. Lett. 54 (2015), pp. 50–55.
- J. Ye and Q. Li, A two-stage linear discriminant analysis via QR-decomposition, IEEE Trans. Pattern Anal. Mach. Intell. 27 (2005), pp. 929–941.
- J. Ye, Z. Zhao, and M. Wu. Discriminative k-means for clustering, in Proceedings of the 20th International Conference on Neural Information Processing Systems (NIPS'07). Curran Associates Inc., Red Hook, NY, USA, 2007, pp. 1649–1656.
- X.S. Yin, S.C. Chen, and E.L. Hu, Regularized soft K-means for discriminant analysis, Neurocomputing 103 (2013), pp. 29–42.
- J.S. Zhang and Y.W. Yeung, Improved possibilistic C-means clustering algorithms, IEEE Trans. Fuzzy Syst. 12 (2004), pp. 209–217.
- X. Zhi, L. Bi, and J. Fan, l2,p-norm based discriminant subspace clustering algorithm, IEEE Access 8 (2020), pp. 76043–76055.
- X. Zhi, J. Fan, and F. Zhao, Fuzzy linear discriminant analysis-guided maximum entropy fuzzy clustering algorithm, Pattern Recognit. 46 (2013), pp. 1604–1615.
- X. Zhi, H. Yan, J. Fan, and S. Zheng, Efficient discriminative clustering via QR decomposition-based linear discriminant analysis, Knowl. Based Syst. 153 (2018), pp. 117–132.