Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A scalable, incremental learning algorithm for classification problems
Computers and Industrial Engineering
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Robust fuzzy relational classifier incorporating the soft class labels
Pattern Recognition Letters
Discriminatively regularized least-squares classification
Pattern Recognition
A simultaneous learning framework for clustering and classification
Pattern Recognition
Semi-supervised learning using label mean
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Efficient Euclidean projections in linear time
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
SemiBoost: Boosting for Semi-Supervised Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
A multiobjective simultaneous learning framework for clustering and classification
IEEE Transactions on Neural Networks
Semi-Supervised Learning
IEEE Transactions on Information Technology in Biomedicine
A supervised clustering and classification algorithm for mining data with mixed variables
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A novel kernelized fuzzy C-means algorithm with application in medical image segmentation
Artificial Intelligence in Medicine
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Optimizing the kernel in the empirical feature space
IEEE Transactions on Neural Networks
A novel radial basis function neural network for discriminant analysis
IEEE Transactions on Neural Networks
The influence of supervised clustering for RBFNN centers definition: a comparative study
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
An efficient and scalable family of algorithms for combining clusterings
Engineering Applications of Artificial Intelligence
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Two main tasks in pattern recognition area are clustering and classification. Owing to their different goals, traditionally these two tasks are treated separately. However, when label information is available, such separate treatment can not fully explore data information. First, classification is not favored by the data cluster structure. Second, clustering is not guided by valuable label information. Third, the relationship of clusters and classes is not revealed. Contrary to this separate learning treatment, simultaneous learning clustering and classification could benefit each other and overcomes these problems. Recently, a simultaneous learning framework SCC was proposed. Through modeling p(class|cluster) classification and clustering mechanism in SCC depend only on cluster centroids. However, it produces severely nonlinear objective, thus has to use a heuristic searching method, modified Particle Swarm Optimization, to find the optimal solution. But it is very slow. Further, modeling p(class|cluster) makes SCC hard to incorporate semi-supervised settings. In this paper, we propose an alternative framework SC^3SR for simultaneous learning. Besides a classifier derived on the original data, another classifier on the newly-formed cluster structure representation is derived as well. Through this classifier, the clustering learning is guided by the label and classification learning is also favored by cluster structure of data. The final objective is continuously differentiable for which some principled optimization algorithms with convergence guaranteed exist. As a result, our algorithm is much faster than SCC. Further, we generalize this framework to semisupervised situation with the idea of manifold regularization and propose SemiSC^3SR algorithm. Our experiments demonstrate the effectiveness of both SC^3SR and SemiSC^3SR.