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dc.contributor.authorZhu, X
dc.contributor.authorLoy, CC
dc.contributor.authorGong, S
dc.date.accessioned2015-02-26T15:12:00Z
dc.date.issued2014-09-24
dc.date.issued2014-09-24
dc.date.issued2014-01-01
dc.identifier.citationIeeexplore.ieee.org. (2017). Constructing Robust Affinity Graphs for Spectral Clustering - IEEE Conference Publication. [online] Available at: http://ieeexplore.ieee.org/document/6909584/?arnumber=6909584&tag=1 [Accessed 28 Sep. 2017].
dc.identifier.isbn9781479951178
dc.identifier.issn1063-6919
dc.identifier.urihttp://qmro.qmul.ac.uk/jspui/handle/123456789/6791
dc.description.abstract© 2014 IEEE.Spectral clustering requires robust and meaningful affinity graphs as input in order to form clusters with desired structures that can well support human intuition. To construct such affinity graphs is non-trivial due to the ambiguity and uncertainty inherent in the raw data. In contrast to most existing clustering methods that typically employ all available features to construct affinity matrices with the Euclidean distance, which is often not an accurate representation of the underlying data structures, we propose a novel unsupervised approach to generating more robust affinity graphs via identifying and exploiting discriminative features for improving spectral clustering. Specifically, our model is capable of capturing and combining subtle similarity information distributed over discriminative feature subspaces for more accurately revealing the latent data distribution and thereby leading to improved data clustering, especially with heterogeneous data sources. We demonstrate the efficacy of the proposed approach on challenging image and video datasets.
dc.format.extent1450 - 1457
dc.titleConstructing robust affinity graphs for spectral clustering
dc.typeConference Proceeding
dc.rights.holder© Copyright 2014 IEEE
dc.identifier.doi10.1109/CVPR.2014.188
pubs.organisational-group/Queen Mary University of London
pubs.organisational-group/Queen Mary University of London/Faculty of Science & Engineering
pubs.organisational-group/Queen Mary University of London/Faculty of Science & Engineering/Electronic Engineering and Computer Science - Staff
pubs.publication-statusPublished


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