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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.
5.3.1 Vantage Point Tree •Heurística propuesta para elegir p: escoger objetos “lejos” del resto •Algoritmo de búsqueda, consultas por rango Calcular Si d ≤ r, añadir p al resultado Si d – r ≤ M, buscar recursivamente en subárbol izquierdo Si d + r > M, buscar recursivamente en subárbol derecho
1 Universidad de Chile Facultad de Ciencias Físicas y Matemáticas Departamento de Ciencias de la Computación Informe Tarea II Estructura de Datos VPT (Vintage Point Tree) Fecha : 24 de Agosto de 2007 Autor : Ismael Andrés Valenzuela Martínez ivalenzu@ing.uchile.cl. 2
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: 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; V 3 Euclidean in this example but works with any metric respecting the triangle inequality!
Among these, vantage point tree (vp-tree) was introduced for information retrieval in metric spaces. Vp-trees have recently shown very good performances for image patch retrieval with respect to the L metric. In this paper we generalize the seminal vp-tree construction and search algo-rithms to the broader class of Bregman divergences.
