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|a 10.1109/TPAMI.2015.2505282
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|a eng
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|a Lu, Na
|e verfasserin
|4 aut
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|a Clustering Tree-Structured Data on Manifold
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|c 2016
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|a Text
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|a Date Completed 07.05.2018
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|a Date Revised 02.12.2018
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Tree-structured data usually contain both topological and geometrical information, and are necessarily considered on manifold instead of euclidean space for appropriate data parameterization and analysis. In this study, we propose a novel tree-structured data parameterization, called Topology-Attribute matrix (T-A matrix), so the data clustering task can be conducted on matrix manifold. We incorporate the structure constraints embedded in data into the non-negative matrix factorization method to determine meta-trees from the T-A matrix, and the signature vector of each single tree can then be extracted by meta-tree decomposition. The meta-tree space turns out to be a cone space, in which we explore the distance metric and implement the clustering algorithm based on the concepts like Fréchet mean. Finally, the T-A matrix based clustering (TAMBAC) framework is evaluated and compared using both simulated data and real retinal images to illustrate its efficiency and accuracy
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|a Journal Article
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|a Research Support, N.I.H., Extramural
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|a Miao, Hongyu
|e verfasserin
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|t IEEE transactions on pattern analysis and machine intelligence
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|g 38(2016), 10 vom: 10. Okt., Seite 1956-68
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|g year:2016
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