Low-rank approximation-based tensor decomposition model for subspace clustering

Electronics Letters, pp. 406-408, 2019.

Cited by: 0|Bibtex|Views25|DOI:https://doi.org/10.1049/el.2018.8240
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Abstract:

To better explore the underlying intrinsic structure of tensorial data, in this Letter, the authors propose a low-rank approximation-based tensor decomposition (LRATD) algorithm for subspace clustering. LRATD aims to seek a low-dimensional intrinsic core tensor representation by projecting the original tensor into a subspace spanned by pr...More

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