Weakness] 1) Originality is limited because the main idea of variable splitting is not new and the algorithm is also not new. 2) Theoretical proofs of existing algorithm might be regarded as some incremental contributions. 3) Experiments are somewhat weak: 3-1) I was wondering why Authors conducted experiments with lambda=1. According to Corollary 1 and 2 lambda should be sufficiently large, however it is completely ignored for experimental setting. Otherwise the proposed algorithm has no difference from [10]. 3-2) In Figure 3, different evaluations are shown in different dataset. It might be regarded as subjectively selected demonstrations. 3-3) I think clustering accuracy is not very significant because there are many other sophisticated algorithms, and initializations are still very important for nice performance. It show just the proposed algorithm is OK for some applications. [Minor points] -- comma should be deleted at line num. 112: "... + \lambda(u-v), = 0 ...". -- "close-form" --> "closed-form" at line num.195-196. --- after feedback --- I understand that the contribution of this paper is a theoretical justification of the existing algorithms proposed in [10]. In that case, the experimental validation with respect to the sensitivity of "lambda" is more important rather than the clustering accuracy. So Fig. 1 in feedback file is nice to add paper if possible. I think dropping symmetry is helpful, however it is not new idea that is already being used. So, it will not change anything in practice to use it. Furthermore, in recent years, almost all application researchers are using some application specific extensions of NMF such as sparse NMF, deep NMF, semi-NMF, and graph-regularized NMF, rather than the standard NMF. Thus, this paper is theoretically interesting as some basic research, but weak from an application perspective. Finally, I changed my evaluation upper one as: "Marginally below the acceptance threshold. I tend to vote for rejecting this submission, but accepting it would not be that bad."
1) Originality is limited because the main idea of variable splitting is not new and the algorithm is also not new.