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231224s2017 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2016.2635657
|2 doi
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|a pubmed24n0894.xml
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|a eng
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| 100 |
1 |
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|a Qi Mao
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Principal Graph and Structure Learning Based on Reversed Graph Embedding
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| 264 |
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|c 2017
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Completed 11.12.2018
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| 500 |
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|a Date Revised 08.10.2019
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a Many scientific datasets are of high dimension, and the analysis usually requires retaining the most important structures of data. Principal curve is a widely used approach for this purpose. However, many existing methods work only for data with structures that are mathematically formulated by curves, which is quite restrictive for real applications. A few methods can overcome the above problem, but they either require complicated human-made rules for a specific task with lack of adaption flexibility to different tasks, or cannot obtain explicit structures of data. To address these issues, we develop a novel principal graph and structure learning framework that captures the local information of the underlying graph structure based on reversed graph embedding. As showcases, models that can learn a spanning tree or a weighted undirected `1 graph are proposed, and a new learning algorithm is developed that learns a set of principal points and a graph structure from data, simultaneously. The new algorithm is simple with guaranteed convergence. We then extend the proposed framework to deal with large-scale data. Experimental results on various synthetic and six real world datasets show that the proposed method compares favorably with baselines and can uncover the underlying structure correctly
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|a Journal Article
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| 650 |
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4 |
|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Li Wang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tsang, Ivor W
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yijun Sun
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 39(2017), 11 vom: 15. Nov., Seite 2227-2241
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
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|g volume:39
|g year:2017
|g number:11
|g day:15
|g month:11
|g pages:2227-2241
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2016.2635657
|3 Volltext
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