An Image Understanding System Using Attributed Symbolic Representation and Inexact Graph-Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Organizing Large Structural Modelbases
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Matching by Discrete Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mixtures of probabilistic principal component analyzers
Neural Computation
Error Correcting Graph Matching: On the Influence of the Underlying Cost Function
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Matching in Computer Vision Using Probabilistic Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficiently Computing Weighted Tree Edit Distance Using Relaxation Labeling
EMMCVPR '01 Proceedings of the Third International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper, we concentrate on graph clustering by using graph spectral features. The leading eigenvectors or the spectrum of graphs and derived feature inter-mode adjacency matrix are used. The embedding methods are the Locality Preserving Projection(LPP) and the mixtures of LPP. The experiment results show that although both of the conventional LPP and the LPP mixtures can separate the different graphs into outstanding clusters, the conventional LPP outperforms the LPP mixtures in the sense of compactness for graph clustering.