Weakly-supervised subspace clustering for sequential data

Being unsupervised methods, subspace clustering algorithms do not generally allow for the incorporation of prior information about a dataset. While this is freedom from prior information can be a strength of such methods, in cases where prior information about a dataset is kn...

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Online Access:http://hdl.handle.net/2047/D20384360
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spelling ndltd-NEU--neu-m046sc23d2021-09-24T05:09:56ZWeakly-supervised subspace clustering for sequential dataBeing unsupervised methods, subspace clustering algorithms do not generally allow for the incorporation of prior information about a dataset. While this is freedom from prior information can be a strength of such methods, in cases where prior information about a dataset is known or given, this information cannot easily be exploited to better model the given dataset. This paper explores a way of extending the K-Subspaces clustering algorithm to incorporate prior information about a dataset and using this information to generate grammars that can provide weak supervision during the training of a subspace clustering-based model. We review the problem of subspace clustering, detail a strategy for adding weak supervision to the existing K-Subspaces algorithm, and compare this new method with other subspace clustering methods in the domain of video segmentation.--Author's abstracthttp://hdl.handle.net/2047/D20384360
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sources NDLTD
description Being unsupervised methods, subspace clustering algorithms do not generally allow for the incorporation of prior information about a dataset. While this is freedom from prior information can be a strength of such methods, in cases where prior information about a dataset is known or given, this information cannot easily be exploited to better model the given dataset. This paper explores a way of extending the K-Subspaces clustering algorithm to incorporate prior information about a dataset and using this information to generate grammars that can provide weak supervision during the training of a subspace clustering-based model. We review the problem of subspace clustering, detail a strategy for adding weak supervision to the existing K-Subspaces algorithm, and compare this new method with other subspace clustering methods in the domain of video segmentation.--Author's abstract
title Weakly-supervised subspace clustering for sequential data
spellingShingle Weakly-supervised subspace clustering for sequential data
title_short Weakly-supervised subspace clustering for sequential data
title_full Weakly-supervised subspace clustering for sequential data
title_fullStr Weakly-supervised subspace clustering for sequential data
title_full_unstemmed Weakly-supervised subspace clustering for sequential data
title_sort weakly-supervised subspace clustering for sequential data
publishDate
url http://hdl.handle.net/2047/D20384360
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