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231226s2023 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2023.3340609
|2 doi
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|a pubmed24n1227.xml
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|a eng
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|a Lu, Han
|e verfasserin
|4 aut
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|a Efficient Multi-View K-Means for Image Clustering
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|c 2023
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
|b c
|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 15.12.2023
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|a published: Print-Electronic
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|a Citation Status Publisher
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|a Nowadays, data in the real world often comes from multiple sources, but most existing multi-view K-Means perform poorly on linearly non-separable data and require initializing the cluster centers and calculating the mean, which causes the results to be unstable and sensitive to outliers. This paper proposes an efficient multi-view K-Means to solve the above-mentioned issues. Specifically, our model avoids the initialization and computation of clusters centroid of data. Additionally, our model use the Butterworth filters function to transform the adjacency matrix into a distance matrix, which makes the model is capable of handling linearly inseparable data and insensitive to outliers. To exploit the consistency and complementarity across multiple views, our model constructs a third tensor composed of discrete index matrices of different views and minimizes the tensor's rank by tensor Schatten p-norm. Experiments on two artificial datasets verify the superiority of our model on linearly inseparable data, and experiments on several benchmark datasets illustrate the performance
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|a Journal Article
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|a Xu, Huafu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Qianqian
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gao, Quanxue
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Ming
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Gao, Xinbo
|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 PP(2023) vom: 13. Dez.
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
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|g volume:PP
|g year:2023
|g day:13
|g month:12
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|u http://dx.doi.org/10.1109/TIP.2023.3340609
|3 Volltext
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