[1] GORBAN A N, ZINOVYEV A Y. PCA and K-means decipher genome [M]//Principal Manifolds for Data Visualization and Dimension Reduction, Lecture Notes in Computational Science and Engineering Volume 58. Berlin: Springer, 2008: 309-323. [2] ZADEH L A. Fuzzy sets [J]. Information and Control, 1965, 8(3): 338-353. [3] BEZDEK J C. Pattern recognition with fuzzy objective function [M]. Norwell: Kluwer Academic Publishers, 1981: 95-154. [4] ZHANG Y, DENG Z, WANG J, et al. Transfer generalized fuzzy c-means clustering algorithm with improved fuzzy partitions by leveraging knowledge [J]. Pattern Recognition and Artificial Intelligence, 2013, 26(10): 975-984. (蒋亦樟, 邓赵红, 王骏, 等. 基于知识利用的迁移学习一般化增强模糊划分聚类算法[J]. 模式识别与人工智能, 2013, 26(10): 975-984.) [5] ZHANG M, YU J. Fuzzy partitional clustering algorithms [J]. Journal of Software, 2004, 15(6): 858-868. (张敏, 于剑. 基于划分的模糊聚类算法[J].软件学报,2004,15(6):858-868.) [6] MIYAMOTO S, MUKAIDONO M. Fuzzy c-means as a regularization and maximum entropy approach[C]//IFSA 1997: Proceedings of the 1997 7th International Fuzzy Systems Association World Congress. Berlin: Springer, 1997: 86-92. [7] LI R-P, MUKAIDONO M. Gaussian clustering method based on maximum-fuzzy-entropy interpretation [J]. Fuzzy Sets and Systems, 1999, 102(2): 253-258. [8] ROSE K, GUREWTIZ E, FOX G. A deterministic annealing approach to clustering [J]. Pattern Recognition Letters, 1990, 11(9): 589-594. [9] PAL N R, PAL K, BEZDEK J C. A mixed c-means clustering model [C]//Proceedings of the Sixth IEEE International Conference on Fuzzy Systems. Piscataway: IEEE, 1997, 1: 11-21. [10] KARAYIANNIS N B. MECA: maximum entropy clustering algorithm [C]//Proceedings of the 1994 IEEE World Congress on Computational Intelligence: Proceedings of the Third IEEE Conference on Fuzzy Systems. Piscataway: IEEE, 1994,1: 630-635. [11] YU J, SHI H, HUANG H, et al. Counterexamples to convergence theorem of maximum-entropy clustering algorithm [J]. Science in China Series F: Information Sciences, 2003, 46(5): 321-326. [12] WANG S, Chung K F L, DENG Z, et al. Robust maximum entropy clustering algorithm with its labeling for outliers [J]. Soft Computing, 2006, 10(7): 555-563. [13] TAO J, CHUNG F, WANG S. On minimum distribution discrepancy support vector machine for domain adaptation [J]. Pattern Recognition, 2012, 45(11): 3962-3984. [14] ZHANG J, WANG S, WANG J. ESVM algorithm in transfer learning data classification [J]. Computer Engineering, 2012, 38(8): 173-176. (张建军, 王士同, 王骏. 迁移学习数据分类中的ESVM算法[J]. 计算机工程, 2012, 38(8): 173-176.) [15] DENG Z, CHOI K-S, CHUNG F-L, et al. Enhanced soft subspace clustering integrating within-cluster and between-cluster information [J]. Pattern Recognition.2010, 43(3): 767-781. [16] PAN S J, YANG Q. A survey on transfer learning [J]. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359. [17] TORREY L, SHAVLIK J, WALKER T, et al. Rule extraction for transfer learning [C]//Rule Extraction from Support Vector Machines: Studies in Computational Intelligence Volume 80. Berlin: Springer, 2000: 67-82. [18] GU Q, ZHOU J. Learning the shared subspace for multi-task clustering and transductive transfer classification [C]//ICDM '09: Proceedings of the 9th IEEE International Conference on Data Mining. Piscataway: IEEE, 2009: 159-168. [19] DAI W, YANG Q, XUE G, et al. Self-taught clustering [C]//ICML '08: Proceedings of the 25th International Conference on Machine Learning. New York: ACM, 2008: 200-207. [20] JIANG W, CHUNG F-L. Transfer spectral clustering [C]//ECML PKDD'12: Proceedings of the 2012 European Conference on Machine Learning and Knowledge Discovery in Databases, LNCS 7524. Berlin: Springer, 2012, 2: 789-803. |