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231224s2017 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2017.2691557
|2 doi
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|a DE-627
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|a eng
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|a Chun-Guang Li
|e verfasserin
|4 aut
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| 245 |
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|a Structured Sparse Subspace Clustering
|b A Joint Affinity Learning and Subspace Clustering Framework
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|c 2017
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 08.03.2019
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|a Date Revised 08.03.2019
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a Subspace clustering refers to the problem of segmenting data drawn from a union of subspaces. State-of-the-art approaches for solving this problem follow a two-stage approach. In the first step, an affinity matrix is learned from the data using sparse or low-rank minimization techniques. In the second step, the segmentation is found by applying spectral clustering to this affinity. While this approach has led to the state-of-the-art results in many applications, it is suboptimal, because it does not exploit the fact that the affinity and the segmentation depend on each other. In this paper, we propose a joint optimization framework - Structured Sparse Subspace Clustering (S3C) - for learning both the affinity and the segmentation. The proposed S3C framework is based on expressing each data point as a structured sparse linear combination of all other data points, where the structure is induced by a norm that depends on the unknown segmentation. Moreover, we extend the proposed S3C framework into Constrained S3C (CS3C) in which available partial side-information is incorporated into the stage of learning the affinity. We show that both the structured sparse representation and the segmentation can be found via a combination of an alternating direction method of multipliers with spectral clustering. Experiments on a synthetic data set, the Extended Yale B face data set, the Hopkins 155 motion segmentation database, and three cancer data sets demonstrate the effectiveness of our approach
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|a Journal Article
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| 700 |
1 |
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|a Chong You
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Vidal, Rene
|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 26(2017), 6 vom: 13. Juni, Seite 2988-3001
|w (DE-627)NLM09821456X
|x 1941-0042
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| 773 |
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|g volume:26
|g year:2017
|g number:6
|g day:13
|g month:06
|g pages:2988-3001
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| 856 |
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|u http://dx.doi.org/10.1109/TIP.2017.2691557
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