While reading the paper "Perturbation analysis for subspace decomposition with applications in subspace-based algorithms" by Zhengyuan Xu, I came across the decomposition technique called Subspace decomposition, where the data matrix $X$ (noise free) is decomposed as: Journal: https://sci-hub.st/10.1109/TSP.2002.804084
$$X = U_s \Lambda_s V_s^H + U_n \Lambda_n V_n^H$$
where $U_s$ and $V_s$ span the column spaces of $X$ and $X^H$ respectively, whereas $U_n$ and $V_n$ span their orthogonal spaces. $\Lambda_s$ and $\Lambda_n$ are corresponding singular values (or eigenvalues).
Could someone explain me this decomposition? I understand it is related to SVD, but I am not sure about the additive funtion here.