K-Nearest Neighbor Search by Random Projection Forests

Fast Approximate Nearest-Neighbor Search with k-Nearest Neighbor Graph

We introduce a new nearest neighbor search algorithm. The algorithm builds a nearest neighbor graph in an offline phase and when queried with a new point, performs hill-climbing starting from a randomly sampled node of the graph. We provide theoretical guarantees for the accuracy and the computational complexity and empirically show the effectiveness of this algorithm.

An Improved K-Nearest Neighbor with Crow Search Algorithm for Feature Selection in Text Documents Classification

The Internet provides easy access to a kind of library resources. However, classification of documents from a large amount of data is still an issue and demands time and energy to find certain documents. Classification of similar documents in specific classes of data can reduce the time for searching the required data, particularly text documents. This is further facilitated by using Artificial...

High-dimensional approximate nearest neighbor: k-d Generalized Randomized Forests

We propose a new data-structure, the generalized randomized k -d forest, or k -d GeRaF, for approximate nearest neighbor searching in high dimensions. In particular, we introduce new randomization techniques to specify a set of independently constructed trees where search is performed simultaneously, hence increasing accuracy. We omit backtracking, and we optimize distance computations, thus ac...

Evaluation Accuracy of Nearest Neighbor Sampling Method in Zagross Forests

Collection of appropriate qualitative and quantitative data is necessary for proper management and planning. Used the suitable inventory methods is necessary and accuracy of sampling methods dependent the inventory net and number of sample point. Nearest neighbor sampling method is a one of distance methods and calculated by three equations (Byth and Riple, 1980; Cotam and Curtis, 1956 and Cota...

Excluded Middle Vantage Point Forests for Nearest Neighbor Search