3 Results Found

This paper concerns a class of robust factorization models of low-rank matrix recovery, which have been widely applied in various fields such as machine learning and imaging sciences. An ${\ell }_1$-loss robust factorized model incorporating the ${\ell }_{}$...

Tensor-based multi-view spectral clustering methods are promising in practical clustering applications. However, most of the existing methods adopt the ${\ell }_{2,1}$ norm to depict the sparsity of the error matrix, and they usually ignore the global str...

The complexity and scale of contemporary datasets are increasing, making the need for reliable and effective subspace processing more pressing. In array signal processing, the quality of the projection matrix and the structure of the noise subspace h...