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A vantage-point tree (or VP tree) is a metric tree that segregates data in a metric space by choosing a position in the space (the "vantage point") and partitioning the data points into two parts: those points that are nearer to the vantage point than a threshold, and those points that are not.
VP-Tree (Vantage Point Tree) is a data structure that enables efficient nearest neighbor search in metric spaces, with applications in machine learning, computer vision, and information retrieval.
Un VPT es un árbol binario en el que cada nodo representa un subconjunto S de individuos del conjunto inicial, utiliza un elemento especial del conjunto llamado pivote (vantage point) para dividir el conjunto S en dos subconjuntos, uno por cada hijo.
Vantage Point Tree (or vp tree) is a space partitioning data structure that allows for efficient querying of nearest neighbors in high dimensional spaces.
A vantage point tree (vp-tree) is a data structure for fast executing of nearest neighbor search queries in a given metric space. This class of trees was first introduced in 1993 in [ 17 ], and is widely used since then.
1 de jul. de 2022 · Vantage Point Trees are constructed by iteratively separating the data points based on their absolute distances from randomly picked centres (VPTs). These "Vantage Points" (VPs) divide the data into halves for each iteration, with half of the data falling inside a certain threshold and the other half falling outside of it.
Vantage Point Tree: Construction 1. Select a vantage point v in X (eg. following a uniform distribution); 2. Compute the distances d(v, xi) between v and each point xi in X; 3. Take the median μ of these distances; 4. Divide X in 2 sets using μ as a threshold: a. X left the set of points closest to v is put at the left b. X right
