Fast geometrical extraction of nearest neighbors from multi-dimensional data

Code

• github.com/FA19C2PC001/FGENN
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Abstract

K-Nearest Neighbor (KNN) algorithm plays a significant role in various fields of data science and machine learning. Most variants of the KNN algorithm involve distance computations and a parameter (K) that represents the required number of neighbors. The recent research regarding distance computations and finding the optimal value of K have made neighborhood extraction a slow process. This research presents a fast geometrical approach for neighborhood extraction from multi-dimensional data. Instead of distance computations, the proposed algorithm creates a geometrical shape based on the number of features of data. This geometrical shape encompasses the reference data point and the neighboring points. The pro- posed algorithm’s efficiency of time, classification, and hashing are evaluated and compared with existing state-of-the-art algorithms.

Reproductions