On low-rank plus sparse matrix sensing - Vary, S (2021)
Expressing a matrix as the sum of a low-rank matrix plus a sparse matrix is a flexible model capturing global and local features in data, and is the foundation of robust principal component analysis (Candès et al., 2011). This thesis is concerned with low-rank plus sparse matrix sensing - the problem of recovering a matrix that is formed as the sum of a low-rank and a sparse matrix, and the two components, from a number of measurements far smaller than the dimensionality of the matrix.