This work considers the conditional mutual information maximization feature selection (CMIM) [42] evaluation method, where the merit of adding X to the current subset S is evaluated according tof(X,Y)=min{I(X,Y),minZ∈SI(X,Y|Z)}, which requires the computation of the conditional...
In the holistic face cognition, both featural and configural information of the face have a mutual influence on one another. The alteration of facial features impacts the spatial relation between those features, and vice versa (Calder2011). de Haas et al. (2016) performed an experiment by pres...
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The mutual information technique was also employed for feature selection. The mutual information technique calculates the entropy of the variables [41]. Thus, it determines the amount of shared information between the variable of interest (target value) and each feature. The RFE and mutual informatio...
In summary, the biPCPG analysis unveils the average influence between industrial and service sectors, efficiently encapsulating the information about the correlation structure of the system. Finally, we provide a Python package named "biPCPG" [35] with its documentation hosted in [36]. The 0.1.0...
In summary, the biPCPG analysis unveils the average influence between industrial and service sectors, efficiently encapsulating the information about the correlation structure of the system. Finally, we provide a Python package named “biPCPG” [35] with its documentation hosted in [36]. The 0.1....
The mutuality matrix at this level gives information about the interrelationship between the feature segments. As shown in Figure 3b, most of these relationships are decreasing mutual relationships because, for every two feature segments, 𝑋𝑖Xi and 𝑋𝑗Xj, 𝑀(𝑋𝑖,𝑋𝑗)<1M(Xi,Xj...