Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
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ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Normalized Cuts and Image Segmentation
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ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the information and representation of non-Euclidean pairwise data
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Robust path-based spectral clustering
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Dimensionality Reduction of Clustered Data Sets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Learning and Pattern Analysis for Image and Video Processing
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Multiway Spectral Clustering with Out-of-Sample Extensions through Weighted Kernel PCA
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enabling scalable spectral clustering for image segmentation
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Parallel Spectral Clustering in Distributed Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-Euclidean or non-metric measures can be informative
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
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This paper introduces the Spectral Clustering Equivalence (SCE) algorithm which is intended to be an alternative to spectral clustering (SC) with the objective to improve both speed and quality of segmentation. Instead of solving for the spectral decomposition of a similarity matrix as in SC, SCE converts the similarity matrix to a column-centered dissimilarity matrix and searches for a pair of the most anticorrelated columns. The orthogonal complement to these columns is then used to create an output feature vector (analogous to eigenvectors obtained via SC), which is used to partition the data into discrete clusters. We demonstrate the performance of SCE on a number of artificial and real datasets by comparing its classification and image segmentation results with those returned by kernel-PCA and Normalized Cuts algorithm. The column-wise processing allows the applicability of SCE to Very Large Scale problems and asymmetric datasets.