Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive dimension reduction for clustering high dimensional data
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Convex Optimization
Document clustering via adaptive subspace iteration
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Discriminative cluster analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive dimension reduction using discriminant analysis and K-means clustering
Proceedings of the 24th international conference on Machine learning
Maximum margin clustering made practical
Proceedings of the 24th international conference on Machine learning
Efficient multiclass maximum margin clustering
Proceedings of the 25th international conference on Machine learning
Non-negative Matrix Factorization on Manifold
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Maximum Margin Clustering with Pairwise Constraints
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Fast evolutionary maximum margin clustering
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Unsupervised and semi-supervised multi-class support vector machines
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Correlated multi-label feature selection
Proceedings of the 20th ACM international conference on Information and knowledge management
Learning a subspace for clustering via pattern shrinking
Information Processing and Management: an International Journal
On building entity recommender systems using user click log and freebase knowledge
Proceedings of the 7th ACM international conference on Web search and data mining
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In text mining, we are often confronted with very high dimensional data. Clustering with high dimensional data is a challenging problem due to the curse of dimensionality. In this paper, to address this problem, we propose an subspace maximum margin clustering (SMMC) method, which performs dimensionality reduction and maximum margin clustering simultaneously within a unified framework. We aim to learn a subspace, in which we try to find a cluster assignment of the data points, together with a hyperplane classifier, such that the resultant margin is maximized among all possible cluster assignments and all possible subspaces. The original problem is transformed from learning the subspace to learning a positive semi-definite matrix, in order to avoid tuning the dimensionality of the subspace. The transformed problem can be solved efficiently via cutting plane technique and constrained concave-convex procedure (CCCP). Since the sub-problem in each iteration of CCCP is joint convex, alternating minimization is adopted to obtain the global optimum. Experiments on benchmark data sets illustrate that the proposed method outperforms the state of the art clustering methods as well as many dimensionality reduction based clustering approaches.