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231225s2022 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2020.3035599
|2 doi
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|a eng
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| 100 |
1 |
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|a You, Chong
|e verfasserin
|4 aut
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| 245 |
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|a Self-Representation Based Unsupervised Exemplar Selection in a Union of Subspaces
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|c 2022
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|a Text
|b txt
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 04.04.2022
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Finding a small set of representatives from an unlabeled dataset is a core problem in a broad range of applications such as dataset summarization and information extraction. Classical exemplar selection methods such as k-medoids work under the assumption that the data points are close to a few cluster centroids, and cannot handle the case where data lie close to a union of subspaces. This paper proposes a new exemplar selection model that searches for a subset that best reconstructs all data points as measured by the l1 norm of the representation coefficients. Geometrically, this subset best covers all the data points as measured by the Minkowski functional of the subset. To solve our model efficiently, we introduce a farthest first search algorithm that iteratively selects the worst represented point as an exemplar. When the dataset is drawn from a union of independent subspaces, our method is able to select sufficiently many representatives from each subspace. We further develop an exemplar based subspace clustering method that is robust to imbalanced data and efficient for large scale data. Moreover, we show that a classifier trained on the selected exemplars (when they are labeled) can correctly classify the rest of the data points
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|a Journal Article
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| 700 |
1 |
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|a Li, Chi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Robinson, Daniel P
|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 pattern analysis and machine intelligence
|d 1979
|g 44(2022), 5 vom: 15. Mai, Seite 2698-2711
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
8 |
|g volume:44
|g year:2022
|g number:5
|g day:15
|g month:05
|g pages:2698-2711
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2020.3035599
|3 Volltext
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|d 44
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