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However, the proposed method and results do not have big impact in practical perspective because the convex regularized matrix factorization itself is very naive, and non-convex regularized low-rank matrix recovery is now widely studied and there are many related works such as truncated nuclear-norm[ex1], weighted nuclear-norm[ex2], capped-l1[ex3], LSP[ex4], SCAD[ex5], and MCP[ex6]. Also in another perspective, greedy rank-increment approach [ex7,ex8,ex9] for low-rank matrix recovery should be referred for discussion. This does not need to estimate initial d unlike regularized matrix factorization methods, and it is usually memory efficient.