Algorithms for clustering data
Algorithms for clustering data
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convergence of alternating optimization
Neural, Parallel & Scientific Computations
Convex Optimization
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Discriminative unsupervised learning of structured predictors
ICML '06 Proceedings of the 23rd 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
Automatic image annotation via local multi-label classification
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
Efficient multiclass maximum margin clustering
Proceedings of the 25th international conference on Machine learning
Large-Scale Clustering through Functional Embedding
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Learning to Recognize Activities from the Wrong View Point
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Fast evolutionary maximum margin clustering
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Subspace maximum margin clustering
Proceedings of the 18th ACM conference on Information and knowledge management
Maximum margin clustering made practical
IEEE Transactions on Neural Networks
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Semi-supervised classification using sparse Gaussian process regression
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Linear time maximum margin clustering
IEEE Transactions on Neural Networks
Fundamenta Informaticae - Intelligent Data Analysis in Granular Computing
Improved MinMax cut graph clustering with nonnegative relaxation
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
An efficient algorithm for maximal margin clustering
Journal of Global Optimization
Proceedings of the 13th International Conference on Information Integration and Web-based Applications and Services
Research history generation from metainformation of research papers using maximum margin clustering
International Journal of Business Intelligence and Data Mining
Convex and scalable weakly labeled SVMs
The Journal of Machine Learning Research
Maximum volume clustering: a new discriminative clustering approach
The Journal of Machine Learning Research
Effective automatic image annotation via integrated discriminative and generative models
Information Sciences: an International Journal
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Maximum margin clustering (MMC) is a recent large margin unsupervised learning approach that has often outperformed conventional clustering methods. Computationally, it involves non-convex optimization and has to be relaxed to different semidefinite programs (SDP). However, SDP solvers are computationally very expensive and only small data sets can be handled by MMC so far. To make MMC more practical, we avoid SDP relaxations and propose in this paper an efficient approach that performs alternating optimization directly on the original non-convex problem. A key step to avoid premature convergence is on the use of SVR with the Laplacian loss, instead of SVM with the hinge loss, in the inner optimization subproblem. Experiments on a number of synthetic and real-world data sets demonstrate that the proposed approach is often more accurate, much faster and can handle much larger data sets.