Position difference-based high-precision nearest neighbor search algorithm

Abstract

The present invention relates to a position difference-based high-precision nearest neighbor search algorithm. The algorithm comprises the steps of setting the value of an i-th component of an i-th reference point in the high dimension distance position difference factors as -1, and setting the values of other components as 1; setting all unit vectors of which the lengths are 1 as the reference points; calculating the distances Disi between the i-th reference point to all data points; ranking the distances Disi, and generating an ordered sequence; calculating the accurate Euclidean distances between a sample point A to a subsequence of which the length is 2k*epsilon, wherein the epsilon is a length adjusting factor of the subsequence; applying a partial ranking algorithm on the obtained distance values to obtain k minimum Euclidean distances; if the nearest neighboring points of all data points to which the reference points are applied are calculated, calculating the high dimension distance position difference factors of all data points and a terminal point, otherwise, enabling i=i+1, and returning to the first step. Under the premise of not increasing the time complexity, the position difference-based high-precision nearest neighbor search algorithm of the present invention enables the precision to be improved, and retains the advantages of being independent of the indexes in a high dimension data set, being efficient and online, etc.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a high-precision neighbor search algorithm based on position differences. Background technique [0002] The k-nearest neighbor search algorithm is used to search the k nearest neighbor points closest to a certain point in the data set. At present, this algorithm has been widely used in many fields such as feature selection, pattern recognition, clustering, noise detection and classification. Among them, as an early k-nearest neighbor search algorithm, the full search algorithm (Full Search Algorithm, referred to as FSA algorithm) is to determine the k nearest neighbor points by calculating the Euclidean distance from the point to be classified to each known category point, so its time complexity higher (O(n 2 )), and poor adaptability. Aiming at the defects of the FSA algorithm, many scholars have proposed algorithms to reduce its time complexity. These algorithms can be rou...

AI Technical Summary

Problems solved by technology

The disadvantage of the above improved algorithm is that as the data dimension increases, its time complexity deteriorates sharply and it loses stability.

For example, Ra and Kim removed impossible data points by calculating the difference between the point to be classified and the average value of each known class point; Lai used triangle inequality and projection value to reduce the computational complexity; Xia et al. After the impact of the search algorithm, the nearest neighbor search algorithm (Location Difference of Multiple Distances Based Nearest Neighbors Searching Algorithm, referred to as the LDMDBA algorithm is proposed. The reference article is: (Xia S, Xiong Z, Luo Y, et al.Location difference of multiple distances based k-nearest neighbors algorithm[J].Knowledge-Based Systems,2015,90(C):99-110.)), the time complexity of the algorithm (O(logdnlogn)) is not only much lower than the FSA algorithm and Most other algorithms, and it has good stability on different high-dimensional data sets, but the disadvantage of this algorithm is that the classification accuracy of some data sets is slightly lower than that of the FSA algorithm

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