Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constrained K-means Clustering with Background Knowledge
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Non-parametric Similarity Measures for Unsupervised Texture Segmentation and Image Retrieval
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Spectral Clustering, With Application To Speech Separation
The Journal of Machine Learning Research
Graph Laplacians and their Convergence on Random Neighborhood Graphs
The Journal of Machine Learning Research
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Multiway Spectral Clustering with Out-of-Sample Extensions through Weighted Kernel PCA
IEEE Transactions on Pattern Analysis and Machine Intelligence
A support vector machine formulation to PCA analysis and its kernel version
IEEE Transactions on Neural Networks
Accelerating spectral clustering with partial supervision
Data Mining and Knowledge Discovery
Spectral clustering: A semi-supervised approach
Neurocomputing
Constrained spectral embedding for K-way data clustering
Pattern Recognition Letters
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A regularized method to incorporate prior knowledge into spectral clustering in the form of pairwise constraints is proposed. This method is based on a weighted kernel principal component analysis (PCA) interpretation of spectral clustering with primal-dual least squares support vector machines (LSSVM) formulations. The weighted kernel PCA framework allows incorporating pairwise constraints into the primal problem leading to a dual eigenvalue problem involving a modified kernel matrix. This modification on the metric is a regularized rank-1 downdate of the original kernel matrix. The clustering model can also be extended to out-of-sample points which becomes important for generalization, predictive purposes and large-scale data. An extension of an existing model selection criterion is also proposed. This extension introduces an additional term to the criterion measuring the constraint fit. Simulation results with toy examples and an image segmentation problem show the applicability of the proposed method.