3 accepted papers
Robust tensor completion aims to recover a tensor from partially observed noisy entries that may be contaminated with large outliers by exploiting its low-rank property. While there exist several robust tensor completion algorithms, their reliance on singular value decomposition (SVD) limits their s…
Tensor ring (TR) decomposition has recently received increased attention due to its superior expressive performance for high-order tensors. However, the applicability of traditional TR decomposition algorithms to real-world applications is hindered by prevalent large data sizes, missing entries, and…
Traditional subspace clustering aims to cluster data lying in a union of linear subspaces. The vectorization of high-dimensional data to 1-D vectors to perform clustering ignores much of the structure intrinsic to such data. To preserve said structure, in this work we exploit clustering in a high-or…