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The other feature, SpSI, is based on a comparison of mutual information measures between selected components of the mammographic images.
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Qualitative aspects of the features were investigated using a normalized point-wise mutual information measure between topics and emotions.
Such an auxiliary input is designed to enlarge the distance measured by the Kullback discrimination information measure between the system models corresponding to the normal and the fault modes.
In general, mutual information measures the dependency between two random variables, that is how much information two variables share and is defined as: I ( X, Y ) = ∑ x ∈ X ∑ x ∈ X p ( x, y ) log p ( x, y ) p ( x ) p ( y ) (1).
The mutual information measures the dependency between two random variables and is mathematically defined as Ileft(X Yright)=sum_{xin varOmega}sum_{yin varOmega }Pleft x,yright){mathrm{log}}_2frac{Pleft x,yright)}{P x P y)}, (1 where P x) and P y) are the marginal probabilities and P x, y) is the joint probability and Ω is the set of nucleotides {A, C, G, U}.
The central idea of this framework relies on between-class information measures defined on multi-gene expression profiles.
Figure 3 Relation between information measures and valence.
Here we study the role of emotions in the communication process building on the relation between information measures and valence.
The work reported here is thus a first step in that the methods employed can in principle be generalized to establish inequalities between information measures on arbitrary graphs.
In the following, we express the main findings of the paper in brief: We explored the relatedness between information measures for graphs.
Normalized mutual information measure estimations between LST and PLAND and ED, PLAND and PD and ED and PD were 0.7679, 0.7650 and 0.7832, respectively.
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