Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Linearized cluster assignment via spectral ordering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning Spectral Clustering, With Application To Speech Separation
The Journal of Machine Learning Research
A tutorial on spectral clustering
Statistics and Computing
Improved Nyström low-rank approximation and error analysis
Proceedings of the 25th international conference on Machine learning
Multiway Spectral Clustering with Out-of-Sample Extensions through Weighted Kernel PCA
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Kernel spectral clustering fits in a constrained optimization framework where the primal problem is expressed in terms of high-dimensional feature maps and the dual problem is expressed in terms of kernel evaluations. An eigenvalue problem is solved at the training stage and projections onto the eigenvectors constitute the clustering model. The formulation allows out-of-sample extensions which are useful for model selection in a learning setting. In this work, we propose a methodology to reveal the hierarchical structure present on the data. During the model selection stage, several clustering model parameters leading to good clusterings can be found. These results are then combined to display the underlying cluster hierarchies where the optimal depth of the tree is automatically determined. Simulations with toy data and real-life problems show the benefits of the proposed approach.