Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions

By Sung-Hyuk Cha. 2007

1,983 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/945c05d471ef987e387f5fedf00c167181da8c8e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Label Distribution Learning (2013, background): “2, d = 1, w1 = [4, 2, 1]T, w2 = [1, 2, 4]T, w3 = [1, 4, 2] T, andλ1 = λ2 = 0.”
On Measures of Entropy and Information (2015, background): “Acknowledgments Useful sources and pedagogical expositions include Cover and Thomas’s classic Elements of Information Theory [8], the inestimable (for some value of truthiness) Wikipedia, and various other reviews [49, 12].”
The human gene damage index as a gene-level approach to prioritizing exome variants (2015, methodology): “We calculated proteins complexity by first extracting the amino acid sequences corresponding to the proteins (27, 28) and then estimating the relative amino acid composition complexity using Clark ’ s distance (63, 64).”
Preserving Link Privacy in Social Network Based Systems (2012, methodology): “In this section, we develop formal metrics to characterize the utility of perturbed graphs, and then analyze the utility of our perturbation algorithm.”
Human mobility: Models and applications (2017, methodology): “The best fit values of the parameters are determined using an optimization algorithm that either minimizes some error function between the model’s estimates and the observed data [169], or maximizes the likelihood function of the observed data given the model’s parameters [170].”
The ecology of the phyllosphere: geographic and phylogenetic variability in the distribution of bacteria on tree leaves (2010, unknown): “To determine the amount of dissimilarity (distance) between any pair of bacterial communities , we employed both a phylogenetic metric [weighted UniFrac (Lozupone and Knight, 2005; Lozupone et al., 2006; Lozupone et al., 2007)] and a taxonomic metric [Kulczynski distance (Cha, 2007)].”
Social Media Text Classification under Negative Covariate Shift (2015, methodology): “In this work, we employed five similarity measures from (Cha, 2007) to gauge the similarity of two vectors.”
A statistical comparative study of different similarity measures of consensus in group decision making (2013, background): “, bn), the following five distance functions have been considered in our study [6, 7, 15, 48]:”
Network Anomaly Detection: Methods, Systems and Tools (2014, background): “Numeric [48] Name Measure, Si(xi, yi) Name Measure, Si(xi, yi)”
Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology (2018, methodology): “It was introduced in relation to the so called Wave Hedges Distance (e.g. Cha, 2007; Prasath, 2017).”
Effects of Distance Measure Choice on K-Nearest Neighbor Classifier Performance: A Review (2017, unknown): “52 When all the vector values are positive, this measure take value between 0 and 1.”
A Comparison Study on Similarity and Dissimilarity Measures in Clustering Continuous Data (2015, background): “The Minkowski family includes Euclidean distance and Manhattan distance, which are particular cases of the Minkowski distance [27 – 29].”
Towards a theory of semantic communication (2011, unknown): “, [10, 40, 50].”
Active Reward Learning from Critiques (2018, background): “Many other measures of distance between two probability distributions [6] may also be used here.”