Algorithms for clustering data
Algorithms for clustering data
Integer and combinatorial optimization
Integer and combinatorial optimization
Kernel principal component analysis
Advances in kernel methods
An Introduction to Variational Methods for Graphical Models
Machine Learning
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automating the Construction of Internet Portals with Machine Learning
Information Retrieval
Linear Programming Boosting via Column Generation
Machine Learning
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Neural Computation
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
A regularization framework for multiple-instance learning
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
Maximum margin clustering made practical
Proceedings of the 24th international conference on Machine learning
Cutting-plane training of structural SVMs
Machine Learning
Unsupervised and semi-supervised multi-class support vector machines
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Multiple view clustering using a weighted combination of exemplar-based mixture models
IEEE Transactions on Neural Networks
Maximum margin clustering on evolutionary data
Proceedings of the 21st ACM international conference on Information and knowledge management
M4L: Maximum margin Multi-instance Multi-cluster Learning for scene modeling
Pattern Recognition
Maximum volume clustering: a new discriminative clustering approach
The Journal of Machine Learning Research
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Maximum margin clustering (MMC) is a newly proposed clustering method which has shown promising performance in recent studies. It extends the computational techniques of support vector machine (SVM) to the unsupervised scenario. Traditionally, MMC is formulated as a nonconvex integer programming problem which makes it difficult to solve. Several methods have been proposed in the literature to solve the MMC problem based on either semidefinite programming (SDP) or alternating optimization. However, these methods are still time demanding when handling large scale data sets, which limits its application in real-world problems. In this paper, we propose a cutting plane maximum margin clustering (CPMMC) algorithm. It first decomposes the nonconvex MMC problem into a series of convex subproblems by making use of the constrained concave-convex procedure (CCCP), then for each subproblem, our algorithm adopts the cutting plane algorithm to solve it. Moreover, we show that the CPMMC algorithm takes O(sn) time to converge with guaranteed accuracy, where n is the number of samples in the data set and s is the sparsity of the data set, i.e., the average number of nonzero features of the data samples. We also derive the multiclass version of our CPMMC algorithm. Experimental evaluations on several real-world data sets show that CPMMC performs better than existing MMC methods, both in efficiency and accuracy.