Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy H Peng, F Long, C Ding IEEE Transactions on pattern analysis and machine intelligence 27 (8), 1226-1238, 2005 | 11678 | 2005 |
Minimum redundancy feature selection from microarray gene expression data C Ding, H Peng Journal of bioinformatics and computational biology 3 (02), 185-205, 2005 | 3486 | 2005 |
Efficient and robust feature selection via joint ℓ2, 1-norms minimization F Nie, H Huang, X Cai, C Ding Advances in neural information processing systems 23, 2010 | 2442 | 2010 |
K-means clustering via principal component analysis C Ding, X He Proceedings of the twenty-first international conference on Machine learning, 29, 2004 | 2122 | 2004 |
Orthogonal nonnegative matrix t-factorizations for clustering C Ding, T Li, W Peng, H Park Proceedings of the 12th ACM SIGKDD international conference on Knowledge …, 2006 | 1559 | 2006 |
Convex and semi-nonnegative matrix factorizations CHQ Ding, T Li, MI Jordan IEEE transactions on pattern analysis and machine intelligence 32 (1), 45-55, 2008 | 1538 | 2008 |
On the equivalence of nonnegative matrix factorization and spectral clustering C Ding, X He, HD Simon Proceedings of the 2005 SIAM international conference on data mining, 606-610, 2005 | 1356 | 2005 |
A min-max cut algorithm for graph partitioning and data clustering CHQ Ding, X He, H Zha, M Gu, HD Simon Proceedings 2001 IEEE international conference on data mining, 107-114, 2001 | 1238 | 2001 |
A min-max cut algorithm for graph partitioning and data clustering CHQ Ding, X He, H Zha, M Gu, HD Simon Proceedings 2001 IEEE international conference on data mining, 107-114, 2001 | 1238 | 2001 |
Multi-class protein fold recognition using support vector machines and neural networks CHQ Ding, I Dubchak Bioinformatics 17 (4), 349-358, 2001 | 1144 | 2001 |
Spectral relaxation for k-means clustering H Zha, X He, C Ding, M Gu, H Simon Advances in neural information processing systems 14, 2001 | 897 | 2001 |
R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization C Ding, D Zhou, X He, H Zha Proceedings of the 23rd international conference on Machine learning, 281-288, 2006 | 821 | 2006 |
Atomic level simulations on a million particles: The cell multipole method for Coulomb and London nonbond interactions HQ Ding, N Karasawa, WA Goddard III The Journal of chemical physics 97 (6), 4309-4315, 1992 | 560 | 1992 |
Bipartite graph partitioning and data clustering H Zha, X He, C Ding, H Simon, M Gu Proceedings of the tenth international conference on Information and …, 2001 | 521 | 2001 |
Symmetric nonnegative matrix factorization for graph clustering D Kuang, C Ding, H Park Proceedings of the 2012 SIAM international conference on data mining, 106-117, 2012 | 497 | 2012 |
Community discovery using nonnegative matrix factorization F Wang, T Li, X Wang, S Zhu, C Ding Data Mining and Knowledge Discovery 22, 493-521, 2011 | 488 | 2011 |
On the equivalence between non-negative matrix factorization and probabilistic latent semantic indexing C Ding, T Li, W Peng Computational Statistics & Data Analysis 52 (8), 3913-3927, 2008 | 415 | 2008 |
The relationships among various nonnegative matrix factorization methods for clustering T Li, C Ding Sixth International Conference on Data Mining (ICDM'06), 362-371, 2006 | 410 | 2006 |
Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization D Wang, T Li, S Zhu, C Ding Proceedings of the 31st annual international ACM SIGIR conference on …, 2008 | 393 | 2008 |
Robust nonnegative matrix factorization using l21-norm D Kong, C Ding, H Huang Proceedings of the 20th ACM international conference on Information and …, 2011 | 386 | 2011 |