1.	The authors claim that the existing PU learning methods will suffer a gradual decline in performance as the dimensionality of the data increases. It would be better if the authors can visualize this effect. This is very important as this is the research motivation of this paper.
2.	Since the authors claim that the high dimensionality is harmful for the PU methods, have the authors tried to firstly implement dimension reduction via some existing approaches and then deploy traditional PU classifiers?
3.	In problem setup, the authors should clarify whether their method belongs to case-control PU learning or censoring PU learning, as their generation ways of P data and U data are quite different.
4.	The proposed algorithm contains Kmeans operation. Note that if there are many examples with high dimension, Kmeans will be very inefficient.
5.	The authors should compare their algorithm with SOTA methods and typical methods on these benchmark datasets.
6.	The figures in this paper are in low quality. Besides, the writing of this paper is also far from perfect.
1. The authors claim that the existing PU learning methods will suffer a gradual decline in performance as the dimensionality of the data increases. It would be better if the authors can visualize this effect. This is very important as this is the research motivation of this paper.