Spectral Clustering using Eigenspectrum Shape Based Nystrom Sampling
@article{Bouneffouf2020SpectralCU,
title={Spectral Clustering using Eigenspectrum Shape Based Nystrom Sampling},
author={Djallel Bouneffouf},
journal={ArXiv},
year={2020},
volume={abs/2007.11416}
}Spectral clustering has shown a superior performance in analyzing the cluster structure. However, its computational complexity limits its application in analyzing large-scale data. To address this problem, many low-rank matrix approximating algorithms are proposed, including the Nystrom method - an approach with proven approximate error bounds. There are several algorithms that provide recipes to construct Nystrom approximations with variable accuracies and computing times. This paper proposes…
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References
SHOWING 1-10 OF 48 REFERENCES
Ensemble Minimum Sum of Squared Similarities sampling for Nyström-based spectral clustering
- Computer Science2016 International Joint Conference on Neural Networks (IJCNN)
- 2016
A new sampling procedure called Ensemble Minimum Sum of Squared Similarities (EMS3) is introduced, which improves on this method by using weight mixtures in subsample selection, yielding more accurate low-rank approximations than existing ensemble Nyström methods.
Sampling with Minimum Sum of Squared Similarities for Nystrom-Based Large Scale Spectral Clustering
- Computer ScienceIJCAI
- 2015
A scalable Nystrom-based clustering algorithm with a new sampling procedure, Minimum Sum of Squared Similarities (MSSS), is proposed and a theoretical analysis of the upper error bound of the algorithm is provided.
Clusterability Analysis and Incremental Sampling for Nyström Extension Based Spectral Clustering
- Computer Science2011 IEEE 11th International Conference on Data Mining
- 2011
The performance of Nyström method is analyzed in terms of cluster ability and an incremental sampling scheme is suggested that outperforms existing sampling schemes on various clustering tasks and image segmentation applications, and its efficiency is comparable withexisting sampling schemes.
Minimum Similarity Sampling Scheme for Nyström Based Spectral Clustering on Large Scale High-Dimensional Data
- Computer ScienceIEA/AIE
- 2014
Experiments show that the proposed sampling scheme outperforms other algorithms when applied in Nystrom based spectral clustering with higher accuracy, and lowers the time consumption for sampling.
Large Scale Spectral Clustering with Landmark-Based Representation
- Computer ScienceAAAI
- 2011
This paper proposes a novel approach, called Landmark-based Spectral Clustering (LSC), for large scale clustering problems, where p representative data points are selected as the landmarks and the spectral embedding of the data can be efficiently computed with the landmark-based representation.
Ensemble Nystrom Method
- Computer ScienceNIPS
- 2009
A new family of algorithms based on mixtures of Nystrom approximation, ensemble Nystrom algorithms, that yield more accurate low-rank approximations than the standard Nystrom method are introduced.
Optimal Landmark Selection for Nyström Approximation
- Computer ScienceICONIP
- 2014
This work proposes a novel approach for optimizing landmark points for the Nystrom approximation by minimizing the approximation error for the input vectors instead of the original kernel.
Spectral grouping using the Nystrom method
- MathematicsIEEE Transactions on Pattern Analysis and Machine Intelligence
- 2004
The contribution of this paper is a method that substantially reduces the computational requirements of grouping algorithms based on spectral partitioning making it feasible to apply them to very large grouping problems.
Spectral methods in machine learning and new strategies for very large datasets
- Computer ScienceProceedings of the National Academy of Sciences
- 2009
Two new algorithms for the approximation of positive-semidefinite kernels based on the Nyström method are presented, each of which demonstrates the improved performance of the approach relative to existing methods.
On sampling-based approximate spectral decomposition
- Computer ScienceICML '09
- 2009
This paper addresses the problem of approximate singular value decomposition of large dense matrices that arises naturally in many machine learning applications and proposes an efficient adaptive sampling technique to select informative columns from the original matrix.