Unique sparse decomposition of low rank matrices
Datasets and Benchmarks Track
Authors: Dian Jin, Xin Bing, Yuqian Zhang

The problem of finding the unique low dimensional decomposition of a given matrix has been a fundamental and recurrent problem in many areas. In this paper, we study the problem of seeking a unique decomposition of a low-rank matrix $Y\in \mathbb{R}^{p\times n}$ that admits a sparse representation. Specifically, we consider $ Y =  AX\in  \mathbb{R}^{p\times n}$ where the matrix $A\in  \mathbb{R}^{p\times r}$ has full column rank, with $r < \min\{n,p\}$, and the matrix $X\in  \mathbb{R}^{r\times n}$ is element-wise sparse.  We prove that this sparse decomposition of $Y$ can be uniquely identified by recovering ground-truth $A$ column by column, up to some intrinsic signed permutation. Our approach relies on solving a nonconvex optimization problem constrained over the unit sphere. Our geometric analysis for the nonconvex optimization landscape shows that any {\em strict} local solution is close to the ground truth solution, and can be recovered by a simple data-driven initialization followed with any second-order descent algorithm. At last, we corroborate these theoretical results with numerical experiments