In the prior literature, they cited the low dimensional embedding methods is the reason of the poor performance of the embedding based methods. In this paper, the author proposed that the final score vector for the labels actually generated by highly non-linear transformation such as thresholding the scores. Thus it is not clear if the low-rank structure of the score vectors directly cause the low-rank on the label vectors. Furthermore, the author uses a simple neural network to mimic the low-dimensional embedding can attain near-perfect training accuracy but generalize poorly and suggesting that overfitting is the root cause of the poor performance of the embedding based methods. This is the first contribution of the paper which breaks the glass ceiling of embedding based methods.