Information Theoretic Measure for Visual Target Distinctness
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the convexity of some divergence measures based on entropy functions
IEEE Transactions on Information Theory
Journal of the American Society for Information Science and Technology
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This paper presents a new method for comparing universities based on information theoretic measures. The research output of each academic institution is represented statistically by an impact-factor histogram. To this aim, for each academic institution we compute the probability of occurrence of a publication with impact factor in different intervals. Assuming the probabilities associated with a pair of academic institutions our objective is to measure the Information Gain between them. To do so, we develop an axiomatic characterization of relative information for predicting institution-institution dissimilarity. We use the Spanish university system as our scenario to test the proposed methodology for benchmarking three universities with the rest as a case study. For each case we use different scientific fields such as Information and Communication Technologies, Medicine and Pharmacy, and Economics and Business as we believe comparisons must take into account their disciplinary context. Finally we validate the Information Gain values obtained for each case with previous studies.