2016
A Subspace Learning Approach for High Dimensional Matrix Decomposition with Efficient Column/Row Sampling
ICML 2016poster
This paper presents a new randomized approach to high-dimensional low rank (LR) plus sparse matrix decomposition. For a data matrix D ∈R^N_1 \times N_2, the complexity of conventional decomposition methods is O(N_1 N_2 r), which limits their usefulness in big data settings (r is the rank of the LR c…