Low-rank approximation-based tensor decomposition model for subspace clustering
Electronics Letters, pp. 406-408, 2019.
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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