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Revisiting Fast Spectral Clustering with Anchor Graph

Abstract: 

In this paper, we revisit the popular affinity matrix based on the anchor graph and point out that the spectral embedding obtained using symmetric normalized Laplacian is only a side view of the bipartite structure. Based on the analysis, we propose Fast Spectral Clustering based on the Random Walk Laplacian (FRWL) method to explicitly balance the popularity of anchors and the independence of data points, which is especially important for clustering of boundary points.

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Submitted On:
6 May 2020 - 11:28pm
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Presentation Slides
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Presenter's Name:
ICASSP20005
Paper Code:
MLSP-P9.7
Document Year:
2020
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large-scale spectral clustering

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[1] , "Revisiting Fast Spectral Clustering with Anchor Graph", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5129. Accessed: Sep. 19, 2020.
@article{5129-20,
url = {http://sigport.org/5129},
author = { },
publisher = {IEEE SigPort},
title = {Revisiting Fast Spectral Clustering with Anchor Graph},
year = {2020} }
TY - EJOUR
T1 - Revisiting Fast Spectral Clustering with Anchor Graph
AU -
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5129
ER -
. (2020). Revisiting Fast Spectral Clustering with Anchor Graph. IEEE SigPort. http://sigport.org/5129
, 2020. Revisiting Fast Spectral Clustering with Anchor Graph. Available at: http://sigport.org/5129.
. (2020). "Revisiting Fast Spectral Clustering with Anchor Graph." Web.
1. . Revisiting Fast Spectral Clustering with Anchor Graph [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5129