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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2021.3114995
|2 doi
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|a pubmed24n1104.xml
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|a (DE-627)NLM331285177
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|a (NLM)34587010
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Wang, Haiyan
|e verfasserin
|4 aut
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| 245 |
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|a Multi-View Learning a Decomposable Affinity Matrix via Tensor Self-Representation on Grassmann Manifold
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|c 2021
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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|a ƒa Online-Ressource
|b cr
|2 rdacarrier
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| 500 |
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|a Date Revised 08.10.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Multi-view clustering aims to partition objects into potential categories by utilizing cross-view information. One of the core issues is to sufficiently leverage different views to learn a latent subspace, within which the clustering task is performed. Recently, it has been shown that representing the multi-view data by a tensor and then learning a latent self-expressive tensor is effective. However, early works mainly focus on learning essential tensor representation from multi-view data and the resulted affinity matrix is considered as a byproduct or is computed by a simple average in Euclidean space, thereby destroying the intrinsic clustering structure. To that end, here we proposed a novel multi-view clustering method to directly learn a well-structured affinity matrix driven by the clustering task on Grassmann manifold. Specifically, we firstly employed a tensor learning model to unify multiple feature spaces into a latent low-rank tensor space. Then each individual view was merged on Grassmann manifold to obtain both an integrative subspace and a consensus affinity matrix, driven by clustering task. The two parts are modeled by a unified objective function and optimized jointly to mine a decomposable affinity matrix. Extensive experiments on eight real-world datasets show that our method achieves superior performances over other popular methods
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|a Journal Article
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| 700 |
1 |
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|a Han, Guoqiang
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Bin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Tao, Guihua
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Cai, Hongmin
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 30(2021) vom: 01., Seite 8396-8409
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
1 |
8 |
|g volume:30
|g year:2021
|g day:01
|g pages:8396-8409
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2021.3114995
|3 Volltext
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|a GBV_ILN_350
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|a AR
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| 952 |
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|d 30
|j 2021
|b 01
|h 8396-8409
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