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

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