TY - GEN
T1 - Fast spectral clustering of data using sequential matrix compression
AU - Chen, Bo
AU - Gao, Bin
AU - Liu, Tie-Van
AU - Chen, Yu-Fu
AU - Ma, Wei-Ying
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2006
Y1 - 2006
N2 - Spectral clustering has attracted much research interest in recent years since it can yield impressively good clustering results. Traditional spectral clustering algorithms first solve an eigenvalue decomposition problem to get the low-dimensional embedding of the data points, and then apply some heuristic methods such as k-means to get the desired clusters. However, eigenvalue decomposition is very time-consuming, making the overall complexity of spectral clustering very high, and thus preventing spectral clustering from being widely applied in large-scale problems. To tackle this problem, different from traditional algorithms, we propose a very fast and scalable spectral clustering algorithm called the sequential matrix compression (SMC) method. In this algorithm, we scale down the computational complexity of spectral clustering by sequentially reducing the dimension of the Laplacian matrix in the iteration steps with very little loss of accuracy. Experiments showed the feasibility and efficiency of the proposed algorithm. © Springer-Verlag Berlin Heidelberg 2006.
AB - Spectral clustering has attracted much research interest in recent years since it can yield impressively good clustering results. Traditional spectral clustering algorithms first solve an eigenvalue decomposition problem to get the low-dimensional embedding of the data points, and then apply some heuristic methods such as k-means to get the desired clusters. However, eigenvalue decomposition is very time-consuming, making the overall complexity of spectral clustering very high, and thus preventing spectral clustering from being widely applied in large-scale problems. To tackle this problem, different from traditional algorithms, we propose a very fast and scalable spectral clustering algorithm called the sequential matrix compression (SMC) method. In this algorithm, we scale down the computational complexity of spectral clustering by sequentially reducing the dimension of the Laplacian matrix in the iteration steps with very little loss of accuracy. Experiments showed the feasibility and efficiency of the proposed algorithm. © Springer-Verlag Berlin Heidelberg 2006.
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U2 - 10.1007/11871842_56
DO - 10.1007/11871842_56
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 354045375
SN - 9783540453758
VL - 4212 LNAI
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 590
EP - 597
BT - Machine Learning: ECML 2006 - 17th European Conference on Machine Learning, Proceedings
PB - Springer Verlag
T2 - 17th European Conference on Machine Learning, ECML 2006
Y2 - 18 September 2006 through 22 September 2006
ER -