16 citations · 87 across the 18 of their papers we have counts for
4 papers · 1 filter
Non-parametric Message Important Measure: Storage Code Design and Transmission Planning for Big Data
Shanyun Liu, Rui She, Pingyi Fan +1
Storage and transmission in big data are discussed in this paper, where message importance is taken into account. Similar to Shannon Entropy and Renyi Entropy, we define non-parame…
Amplifying Inter-message Distance: On Information Divergence Measures in Big Data
Rui She, Shanyun Liu, Pingyi Fan
Message identification (M-I) divergence is an important measure of the information distance between probability distributions, similar to Kullback-Leibler (K-L) and Renyi divergenc…
Shannon Shakes Hands with Chernoff: Big Data Viewpoint On Channel Information Measures
Shanyun Liu, Rui She, Jiaxun Lu +1
Shannon entropy is the most crucial foundation of Information Theory, which has been proven to be effective in many fields such as communications. Renyi entropy and Chernoff inform…
Focusing on a Probability Element: Parameter Selection of Message Importance Measure in Big Data
Rui She, Shanyun Liu, Yunquan Dong +1
Message importance measure (MIM) is applicable to characterize the importance of information in the scenario of big data, similar to entropy in information theory. In fact, MIM wit…