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In supervised global distance metric learning, the representative work formulates distance metric learning as a constrained convex programming problem [ 27].
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Below, we formulate distance to bifurcation manifolds (first introduced in [ 15]) in terms of a forward operator and an l2 functional.
In Euclidean geometry, the dot product between two vectors formulates the distance and linear similarity between them.
The key ideas are using level set functions to describe the shapes of arbitrary irregular embedding components and resorting to the concept of structural skeleton to formulate the distance control constraints explicitly.
Glunt et al. [6] formulate the Euclidean distance matrix problem as a constrained least distance problem in which the constraint is the intersection of two convex sets.
Moreover, a structure stress based fusion scheme is applied to formulate the proposed distance metric, i.e. CSG-EMD, for gesture recognition.
Therefore, two constraints are formulated, considering the distance from a new station to adjacent stations (i.e., both existing and new stations).
In short, an incoming pixel vector,, is the center of a neighborhood of size which is checked for irregularity via the distance formulated above.
Therefore, we formulated a probabilistic distance function to accommodate this property.
A mathematical program with equilibrium constraints (MPEC) is developed to formulate the optimal joint distance and time toll design problem.
In order to calculate the distance, we formulated a relationship between the focal length (f) and the distance to a landmark (r).
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