[7] Li Y, Zhen C, Shi X et al. Hyperspectral image classification based on entropy-weighted K-means global information clustering[J]. Chinese Journal of Image and Graphics, 24, 630-638(2019).
[8] Bezdek J C. Modified objective function algorithms[M]. ∥Pattern recognition with fuzzy objective function algorithms. Boston: Springer, 155-201(1981).
[9] Chen S, Zhang D. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure[J]. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 34, 1907-1916(2004).
[10] Ng A Y, Jordan M I, Weiss Y. On spectral clustering:analysis and an algorithm. [C]∥ 14th International Conference on Neural Infomation Processing Systems: Natural and Synthetic, December 3-8, 2001, Vancouver, Canada. New York: Curran Associates, 849-856(2001).
[11] Yang X J, Yu W Z, Wang R et al. Fast spectral clustering learning with hierarchical bipartite graph for large-scale data[J]. Pattern Recognition Letters, 130, 345-352(2020).
[12] Jebara T, Wang J, Chang S F. Graph construction and b-matching for semi-supervised learning[C]∥, 441-448(2009).
[14] Li Y, Nie F, Huang H et al. Large-scale multi-view spectral clustering via bipartite graph. [C]∥Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, January 25-30, 2015, Austin, Texas, USA. [S.l:s.n.], 2750-2756(2015).
[15] Cai D, Chen X L. Large scale spectral clustering via landmark-based sparse representation[J]. IEEE Transactions on Cybernetics, 45, 1669-1680(2015).
[16] Zhou X M, Pan D. Research on large scale image indexing technology based on PCA-binary tree[J]. Journal of Southwest China Normal University (Natural Science Edition), 44, 57-62(2019).
[17] Liu Y X, Zhang Z H, Zhang Y M. Image denoising based on K-means clustering and binary tree decision[J]. Computer Engineering & Science, 35, 118-123(2013).
[18] Zhu W, Nie F P, Li X L. Fast spectral clustering with efficient large graph construction. [C]∥2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), March 5-9,2017, New Orleans, LA, USA. New York: IEEE, 2492-2496(2017).
[19] Zhou Y C, Peng J T. Chen C L P. Dimension reduction using spatial and spectral regularized local discriminant embedding for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 53, 1082-1095(2015).
[20] Nie F, Zhu W, Li X. Unsupervised large graph embedding. [C]∥ Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, February 4-9, 2017, San Francisco, California, USA.[S.l:s.n.], 2422-2428(2017).
[21] Nie F P, Wang X Q, Jordan M I et al. The constrained Laplacian rank algorithm for graph-based clustering. [C]∥ Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA. [S.l:s.n.], 1969-1976(2016).
[23] Wang R, Nie F P, Hong R C et al. Fast and orthogonal locality preserving projections for dimensionality reduction[J]. IEEE Transactions on Image Processing, 26, 5019-5030(2017).