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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.
A vantage point tree (VP-Tree) is a data structure used for efficiently searching for nearest neighbors in metric spaces. It organizes data points in a tree structure based on their distances to a chosen vantage point. This enables faster search operations compared to traditional linear search methods.
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.
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 (or vp tree) is a space partitioning data structure that allows for efficient querying of nearest neighbors in high dimensional spaces.
The algorithm uses a Multi-Vantage Point (MVP) tree data structure to store the pairs, and to perform look-ups based on the TLSH hash and on the TLSH distance metric.
Distance-based methods that use the LSH to define the distance metric between points, and use indexing structures such as Vantage-Point-Trees (VPT) [25, 26] to do fast nearest neighbor search (such as HAC-T [18]). Collision-based methods are ideal when the LSH buckets have a good fit with the desired clusters.
