Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/101454
Citations
Scopus Web of Science® Altmetric
?
?
Type: Journal article
Title: Shifting multi-hypergraphs via collaborative probabilistic voting
Author: Wang, Y.
Lin, X.
Wu, L.
Zhang, Q.
Zhang, W.
Citation: Knowledge and Information Systems, 2016; 46(3):515-536
Publisher: Springer
Issue Date: 2016
ISSN: 0219-1377
0219-3116
Statement of
Responsibility: 
Yang Wang, Xuemin Lin, Lin Wu, Qing Zhang, Wenjie Zhang
Abstract: Graphs are widely utilized to characterize the complex relationship among big data. Graph mode seeking is of great importance to many applications in data mining and machine learning era, and it attracts a number of approaches. Typically, existing methods, e.g., graph shift, focus on shifting vertices based on pairwise edges (i.e., an edge connecting two vertices) to find the cohesively dense subgraph. However, they overlooked the semantics of these subgraphs, resulting into undesirable results to the users in specific applications, e.g., saliency detection. In this paper, we propose a novel paradigm aimed at shifting high-order edges (i.e., hyperedges) to deliver graph modes, via a novel probabilistic voting strategy. As a result, the generated graph modes based on dense subhypergraphs may more accurately capture the semantics of objects besides the self-cohesiveness requirement. It is widely known that data objects are always described by multiple features or multi-views, e.g., an image has a color feature and shape feature, where the information provided by all views are complementary to each other. Based on such fact, we propose another novel technique of shifting multiple hypergraphs, each of which corresponds to one view, by conducting a novel collaborative probabilistic voting strategy, named SMHCPV, so as to further improve the performance over hypergraph shift method. Extensive experiments are conducted on both synthetic and real-world datasets to validate the superiority of our proposed technique for both hypergraph shift and SMHCPV.
Keywords: Hypergraph shift; multi-view; collaborative probabilistic voting
Rights: © Springer-Verlag London 2015
DOI: 10.1007/s10115-015-0833-8
Grant ID: http://purl.org/au-research/grants/arc/DP150102728
http://purl.org/au-research/grants/arc/DP140103578
http://purl.org/au-research/grants/arc/DP150103071
NSFC61021004
Appears in Collections:Aurora harvest 3
Computer Science publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.