Abstract: Distance function is a main metrics of measuring the affinity between two data points in machine learning. Extant distance functions often provide unreachable distance values in real ...
Abstract: Effective distance functions in high dimensional data space are very important in solutions for many data mining problems. Recent research has shown that if the Pearson variation of the ...
Transactions of the American Mathematical Society, Vol. 359, No. 12 (Dec., 2007), pp. 5725-5759 (35 pages) Let the space ℝⁿ be endowed with a Minkowski structure M (that is, M: ℝⁿ → [0, + ∞]) is the ...
I will define a new distance function on an unoriented 3D point set and describe how it may be used to reconstruct a surface approximating these points. This distance function is shown to be a ...
After being unsuccessful at finding a pre-written function online, I created my own function for calculating the surface distance between two segmentations in Python. For two image-segmentations, this ...
Geodesic distance from a single point on a surface. The heat method allows distance to be rapidly updated for new source points or curves. We introduce the heat method for solving the single- or ...
1 Department of Genetic Engineering, Cinvestav Irapuato, Irapuato, Mexico. 2 Department of Biochemistry and Biotechnology, Cinvestav Irapuato, Irapuato, Mexico. Heatmap cluster figures are often used ...
What if instead of defining a mesh as a series of vertices and edges in a 3D space, you could describe it as a single function? The easiest function would return the signed distance to the closest ...
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