@inproceedings{8d9facbc9a0b482785459000323f815f,
title = "Clustering of tree-structured data",
abstract = "Tree-structured data conveys both topological and geometrical information, which is strongly non-Euclidean and thus need be considered on manifold for parameterization and analysis. To address this problem and perform tree-structured data clustering, a novel parameterization method using the Topology-Attribute matrix (T-A matrix) is proposed which could enable tree analysis on matrix manifold. Then a nonnegative matrix factorization (NMF) method with structure constraint from trees is developed to mine the subspace of tree-structured data, which we call meta-tree space. The clustering task is conducted in the meta-tree space based on the concept of Fr{\'e}chet mean. The proposed method is evaluated using both simulated data and real retinal images.",
keywords = "clustering, nonnegative matrix factorization, Tree",
author = "Na Lu and Yidan Wu",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics ; Conference date: 08-08-2015 Through 10-08-2015",
year = "2015",
month = sep,
day = "28",
doi = "10.1109/ICInfA.2015.7279471",
language = "英语",
series = "2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1210--1215",
booktitle = "2015 IEEE International Conference on Information and Automation, ICIA 2015 - In conjunction with 2015 IEEE International Conference on Automation and Logistics",
}