Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
On the alleviation of the problem of local minima in back-propagation
Proceedings of second world congress on Nonlinear analysts
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Fuzzy C-Means Clustering Algorithm Based on Kernel Method
ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning a kernel function for classification with small training samples
ICML '06 Proceedings of the 23rd international conference on Machine learning
Co-training for predicting emotions with spoken dialogue data
ACLdemo '04 Proceedings of the ACL 2004 on Interactive poster and demonstration sessions
Feature-based approach to semi-supervised similarity learning
Pattern Recognition
Kernel selection forl semi-supervised kernel machines
Proceedings of the 24th international conference on Machine learning
A unified framework for semi-supervised dimensionality reduction
Pattern Recognition
Partially supervised clustering for image segmentation
Pattern Recognition
Mercer kernel-based clustering in feature space
IEEE Transactions on Neural Networks
A hybrid particle swarm optimization approach for clustering and classification of datasets
Knowledge-Based Systems
A quality driven Hierarchical Data Divisive Soft Clustering for information retrieval
Knowledge-Based Systems
Semi-supervised clustering with discriminative random fields
Pattern Recognition
Semi-supervised fuzzy clustering with metric learning and entropy regularization
Knowledge-Based Systems
Random projection ensemble learning with multiple empirical kernels
Knowledge-Based Systems
Speeding-up the kernel k-means clustering method: A prototype based hybrid approach
Pattern Recognition Letters
Unsupervised non-parametric kernel learning algorithm
Knowledge-Based Systems
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Semi-supervised clustering algorithms aim to improve the clustering accuracy under the supervisions of a limited amount of labeled data. Since kernel-based approaches, such as kernel-based fuzzy c-means algorithm (KFCM), have been successfully used in classification and clustering problems, in this paper, we propose a novel semi-supervised clustering approach using the kernel-based method based on KFCM and denote it the semi-supervised kernel fuzzy c-mean algorithm (SSKFCM). The objective function of SSKFCM is defined by adding classification errors of both the labeled and the unlabeled data, and its global optimum has been obtained through repeatedly updating the fuzzy memberships and the optimized kernel parameter. The objective function may have more than one local optimum, so we employ a function transformation technique to reformulate the objective function after a local minimum has been obtained, and select the best optimum as the solution to the objective function. Experimental results on both the artificial and several real data sets show SSKFCM performs better than its conventional counterparts and it achieves the best accurate clustering results when the parameter is optimized.