暂无分享,去创建一个
[1] Vin de Silva,et al. Tensor rank and the ill-posedness of the best low-rank approximation problem , 2006, math/0607647.
[2] S. P. Lloyd,et al. Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.
[3] Alexander J. Smola,et al. Learning with kernels , 1998 .
[4] George M. Church,et al. Biclustering of Expression Data , 2000, ISMB.
[5] Amit Kumar,et al. A simple linear time (1 + /spl epsiv/)-approximation algorithm for k-means clustering in any dimensions , 2004, 45th Annual IEEE Symposium on Foundations of Computer Science.
[6] Marcel R. Ackermann,et al. Clustering for metric and non-metric distance measures , 2008, SODA '08.
[7] J. Hartigan. Direct Clustering of a Data Matrix , 1972 .
[8] Richard Nock,et al. Mixed Bregman Clustering with Approximation Guarantees , 2008, ECML/PKDD.
[9] Ran El-Yaniv,et al. Multi-way distributional clustering via pairwise interactions , 2005, ICML.
[10] Matthias Hein,et al. Hilbertian Metrics and Positive Definite Kernels on Probability Measures , 2005, AISTATS.
[11] Arindam Banerjee,et al. Approximation Algorithms for Bregman Clustering Co-clustering and Tensor Clustering , 2008 .
[12] Chris Ding,et al. Proceedings of the 2nd Workshop on Data Mining using Matrices and Tensors , 2009, KDD 2009.
[13] Gemma C. Garriga,et al. An approximation ratio for biclustering , 2008, Inf. Process. Lett..
[14] Suvrit Sra,et al. Minimum Sum-Squared Residue based clustering of Gene Expression Data , 2004 .
[15] Joseph T. Chang,et al. Spectral biclustering of microarray data: coclustering genes and conditions. , 2003, Genome research.
[16] Johannes Blömer,et al. Coresets and approximate clustering for Bregman divergences , 2009, SODA.
[17] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.
[18] Andrew McGregor,et al. Finding Metric Structure in Information Theoretic Clustering , 2008, COLT.
[19] Arlindo L. Oliveira,et al. Biclustering algorithms for biological data analysis: a survey , 2004, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[20] Anirban Dasgupta,et al. Approximation algorithms for co-clustering , 2008, PODS.
[21] Vincent Kanade,et al. Clustering Algorithms , 2021, Wireless RF Energy Transfer in the Massive IoT Era.
[22] Y. Censor,et al. Parallel Optimization:theory , 1997 .
[23] E. Lander,et al. MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia , 2002, Nature Genetics.
[24] C. Berg,et al. Harmonic Analysis on Semigroups: Theory of Positive Definite and Related Functions , 1984 .
[25] Tamara G. Kolda,et al. Scalable Tensor Decompositions for Multi-aspect Data Mining , 2008, 2008 Eighth IEEE International Conference on Data Mining.
[26] Arindam Banerjee,et al. Multi-way Clustering on Relation Graphs , 2007, SDM.
[27] Sergei Vassilvitskii,et al. k-means++: the advantages of careful seeding , 2007, SODA '07.
[28] Philip S. Yu,et al. Unsupervised learning on k-partite graphs , 2006, KDD '06.
[29] Inderjit S. Dhillon,et al. Information-theoretic co-clustering , 2003, KDD '03.
[30] Inderjit S. Dhillon,et al. Clustering with Bregman Divergences , 2005, J. Mach. Learn. Res..
[31] I. Dhillon,et al. Coclustering of Human Cancer Microarrays Using Minimum Sum-Squared Residue Coclustering , 2008, IEEE/ACM Transactions on Computational Biology and Bioinformatics.
[32] Tamir Hazan,et al. Multi-way Clustering Using Super-Symmetric Non-negative Tensor Factorization , 2006, ECCV.
[33] Andrzej Stachurski,et al. Parallel Optimization: Theory, Algorithms and Applications , 2000, Scalable Comput. Pract. Exp..
[34] Pietro Perona,et al. Beyond pairwise clustering , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[35] Alan M. Frieze,et al. Clustering Large Graphs via the Singular Value Decomposition , 2004, Machine Learning.
[36] I. J. Schoenberg,et al. Metric spaces and positive definite functions , 1938 .
[37] S. D. Cutkosky,et al. Multilinear Algebra , 2019, Differential Forms.
[38] Inderjit S. Dhillon,et al. A generalized maximum entropy approach to bregman co-clustering and matrix approximation , 2004, J. Mach. Learn. Res..
[39] L. Bregman. The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming , 1967 .
[40] Venu Madhav Govindu,et al. A tensor decomposition for geometric grouping and segmentation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).