Journal of the ACM (JACM)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principles of data mining
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)
Efficient svm training using low-rank kernel representations
The Journal of Machine Learning Research
Kernel independent component analysis
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Methods for Measuring Independence
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
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
Multiway Spectral Clustering with Out-of-Sample Extensions through Weighted Kernel PCA
IEEE Transactions on Pattern Analysis and Machine Intelligence
New spectral methods for ratio cut partitioning and clustering
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
| Hi-index | 7.29 |
A novel sparse spectral clustering method using linear algebra techniques is proposed. Spectral clustering methods solve an eigenvalue problem containing a graph Laplacian. The proposed method exploits the structure of the Laplacian to construct an approximation, not in terms of a low rank approximation but in terms of capturing the structure of the matrix. With this approximation, the size of the eigenvalue problem can be reduced. To obtain the indicator vectors from the eigenvectors the method proposed by Zha et al. (2002) [26], which computes a pivoted LQ factorization of the eigenvector matrix, is adapted. This formulation also gives the possibility to extend the method to out-of-sample points.