GO Decomposition (GoDec) Approach to Finding Low Rank and Sparsity Matrices for Hyperspectral Target Detection

Low rank and sparsity matrix decomposition (LRaSMD) has received considerable interest lately. One of effective methods is called go decomposition (GoDec) which finds low rank and sparse matrices iteratively subject to a predetermined low rank order, $m$ and a sparsity cardinality, $k$ , In order to resolve issue of the empirically determined $m$ and $k$ , the well-known virtual dimensionality (VD) and a minimax-singular value decomposition (MX-SVD) developed in maximum orthogonal complement algorithm (MOCA) are used for this purpose. The constrained energy minimization (CEM) is used for experiments to demonstrate that the GoDec with VD and MX-SVD performs very effectively.