Connectivity Graphs and Clustering with Similarity Functions

2009 ◽  
pp. 109-122
Author(s):  
Wesam Ashour Barbakh ◽  
Ying Wu ◽  
Colin Fyfe
Keyword(s):  
Similarity Functions

scholarly journals Preservers of unitary similarity functions on Lie products of matrices

2016 ◽  
Vol 498 ◽  
pp. 160-180 ◽  
Author(s):  
Jianlian Cui ◽  
Chi-Kwong Li ◽  
Yiu-Tung Poon
Keyword(s):  
Unitary Similarity ◽  
Similarity Functions ◽  
Products Of Matrices

scholarly journals Extension to C-means Algorithm for the Use of Similarity Functions

1999 ◽  
pp. 354-359 ◽  
Author(s):  
Javier Raymundo García-Serrano ◽  
José Francisco Martínez-Trinidad
Keyword(s):  
Similarity Functions

Preservers of radial unitary similarity functions on products of operators

2018 ◽  
Vol 542 ◽  
pp. 484-500 ◽  
Author(s):  
M. Bendaoud ◽  
A. Benyouness ◽  
M. Sarih ◽  
S. Sekkat
Keyword(s):  
Unitary Similarity ◽  
Similarity Functions

scholarly journals Gradient representations in ReLU networks as similarity functions

2021 ◽  
Author(s):  
Bálint Daróczy ◽  
Dániel Rácz
Keyword(s):  
Similarity Functions

Two Experiments on Using a Scintillometer to Infer the Surface Fluxes of Momentum and Sensible Heat

2012 ◽  
Vol 51 (9) ◽  
pp. 1685-1701 ◽  
Author(s):  
Edgar L Andreas
Keyword(s):  
Eddy Covariance ◽  
Correlation Coefficients ◽  
Arctic Sea Ice ◽  
Surface Fluxes ◽  
Index Structure ◽  
Quality Data ◽  
Sensible Heat ◽  
Traditional Use ◽  
Similarity Functions ◽  
Essential Question

AbstractA traditional use of scintillometry is to infer path-averaged values of the turbulent surface fluxes of sensible heat Hs and momentum τ (, where ρ is air density and u* is the friction velocity). Many scintillometer setups, however, measure only the path-averaged refractive-index structure parameter ; the wind information necessary for inferring u* and Hs comes from point measurements or is absent. The Scintec AG SLS20 surface-layer scintillometer system, however, measures both and the inner scale of turbulence ℓ0, where ℓ0 is related to the dissipation rate of turbulent kinetic energy ɛ. The SLS20 is thus presumed to provide path-averaged estimates of both u* and Hs. This paper describes comparisons between SLS20-derived estimates of u* and Hs and simultaneous eddy-covariance measurements of these quantities during two experiments: one, over Arctic sea ice; and a second, over a midlatitude land site during spring. For both experiments, the correlation between scintillometer and eddy-covariance fluxes is reasonable: correlation coefficients are typically above 0.7 for the better-quality data. For both experiments, though, the scintillometer usually underestimates u* and underestimates the magnitude of Hs when compared with the corresponding eddy-covariance values. The data also tend to be more scattered when < 10−14 m−2/3: the signal-to-noise ratio for scintillometer-derived fluxes decreases as decreases. An essential question that arises during these comparisons is what similarity functions to use for inferring fluxes from the scintillometer and ℓ0 measurements. The paper thus closes by evaluating whether any of four candidate sets of similarity functions is consistent with the scintillometer data.

Popularised Similarity Function for Effective Collaborative Filtering Recommendations

2020 ◽  
Vol 10 (1) ◽  
pp. 34-47
Author(s):  
Abba Almu ◽  
Abubakar Roko ◽  
Aminu Mohammed ◽  
Ibrahim Saidu
Keyword(s):  
Collaborative Filtering ◽  
Experimental Studies ◽  
Similarity Function ◽  
Similarity Functions ◽  
Rating Prediction

The existing similarity functions use the user-item rating matrix to process similar neighbours that can be used to predict ratings to the users. However, the functions highly penalise high popular items which lead to predicting items that may not be of interest to active users due to the punishment function employed. The functions also reduce the chances of selecting less popular items as similar neighbours due to the items with common ratings used. In this article, a popularised similarity function (pop_sim) is proposed to provide effective recommendations to users. The pop_sim function introduces a modified punishment function to minimise the penalty on high popular items. The function also employs a popularity constraint which uses ratings threshold to increase the chances of selecting less popular items as similar neighbours. The experimental studies indicate that the proposed pop_sim is effective in improving the accuracy of the rating prediction in terms of not only lowering the MAE but also the RMSE.

scholarly journals Prediction by Empirical Similarity via Categorical Regressors

2019 ◽  
Vol 1 (2) ◽  
pp. 641-652 ◽  
Author(s):  
Jeniffer Duarte Sanchez ◽  
Leandro C. Rêgo ◽  
Raydonal Ospina
Keyword(s):  
Similarity Measures ◽  
Weighted Average ◽  
Distance Metrics ◽  
Delivery Methods ◽  
Similarity Functions ◽  
Parametric Regression ◽  
Continuous Response ◽  
Low Dimension ◽  
Prediction Problems ◽  
Learning Data

A quantifier of similarity is generally a type of score that assigns a numerical value to a pair of sequences based on their proximity. Similarity measures play an important role in prediction problems with many applications, such as statistical learning, data mining, biostatistics, finance and others. Based on observed data, where a response variable of interest is assumed to be associated with some regressors, it is possible to make response predictions using a weighted average of observed response variables, where the weights depend on the similarity of the regressors. In this work, we propose a parametric regression model for continuous response based on empirical similarities for the case where the regressors are represented by categories. We apply the proposed method to predict tooth length growth in guinea pigs based on Vitamin C supplements considering three different dosage levels and two delivery methods. The inferential procedure is performed through maximum likelihood and least squares estimation under two types of similarity functions and two distance metrics. The empirical results show that the method yields accurate models with low dimension facilitating the parameters’ interpretation.

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