Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
An improved spectral graph partitioning algorithm for mapping parallel computations
SIAM Journal on Scientific Computing
Spectral partitioning with multiple eigenvectors
Discrete Applied Mathematics - Special volume on VLSI
ACM Computing Surveys (CSUR)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Feature Selection for Clustering
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Feature Selection for Clustering - A Filter Solution
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Kernel Principle Component Analysis in Pixels Clustering
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Learning Spectral Clustering, With Application To Speech Separation
The Journal of Machine Learning Research
Spectral clustering with eigenvector selection
Pattern Recognition
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object trajectory clustering via tensor analysis
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Supervised nonlinear dimensionality reduction for visualization and classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Spectral clustering with more than K eigenvectors
Neurocomputing
Self-adjust local connectivity analysis for spectral clustering
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Automatic image segmentation using constraint learning and propagation
Digital Signal Processing
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Ng-Jordan-Weiss (NJW) method is one of the most widely used spectral clustering algorithms. For a K clustering problem, this method partitions data using the largest K eigenvectors of the normalized affinity matrix derived from the dataset. It has been demonstrated that the spectral relaxation solution of K-way grouping is located on the subspace of the largest K eigenvectors. However, we find from a lot of experiments that the top K eigenvectors cannot always detect the structure of the data for real pattern recognition problems. So it is necessary to select eigenvectors for spectral clustering. We propose an eigenvector selection method based on entropy ranking for spectral clustering (ESBER). In this method, first all the eigenvectors are ranked according to their importance on clustering, and then a suitable eigenvector combination is obtained from the ranking list. In this paper, we propose two strategies to select eigenvectors in the ranking list of eigenvectors. One is directly adopting the first K eigenvectors in the ranking list. Different to the largest K eigenvectors of NJW method, these K eigenvectors are the most important eigenvectors among all the eigenvectors. The other eigenvector selection strategy is to search a suitable eigenvector combination among the first Km (KmK) eigenvectors in the ranking list. The eigenvector combination obtained by this strategy can reflect the structure of the original data and lead to a satisfying spectral clustering result. Furthermore, we also present computational complexity reduction strategies for ESBER method to deal with large-scale datasets. We have performed experiments on UCI benchmark datasets, MNIST handwritten digits datasets, and Brodatz texture datasets, adopting NJW method for a baseline comparison. The experimental results show that ESBER method is more robust than NJW method. Especially, ESBER method with the latter eigenvector selection strategy can obtain satisfying clustering results in most cases.