Data Dimensionality Reduction Method Based on Neighborhood Similarity

Abstract

The invention discloses a data dimension reduction method based on neighborhood similarity and mainly solves the problem that an existing method only uses the Euclidean distance to measure a sample neighborhood structure, so that recognition results are non-ideal when a data structure is not balanced. The data dimension reduction method comprises the following realization steps: (1) inputting and normalizing data and randomly initializing a basis matrix and a coefficient matrix; (2) calculating a diagonal covariance matrix of a sample; (3) calculating KL (Kullback-Leibler) divergence through the diagonal covariance matrix; (4) calculating neighborhood sample similarity through the KL dispersion degree; (5) calculating a neighborhood class label distribution matrix of the sample; (6) calculating neighborhood class label similarity through the neighborhood class label distribution matrix; (7) calculating neighborhood similarity through the neighborhood sample similarity and the neighborhood class label similarity; (8) applying to iterative criterions according to the neighborhood similarity to obtain the basis matrix and the coefficient matrix after dimension reduction. The data dimensionality reduction method is high in accuracy rate and can effectively perform feature extraction and dimensionality reduction on data and be used for data and image processing.

Description

technical field [0001] The invention belongs to the technical field of data processing, in particular to a data dimensionality reduction method, which can be used for data and computer image recognition. Background technique [0002] The rapid development of science and technology in recent years has resulted in an explosion in the amount and availability of raw data. With the development of sensor and computer technology, more and more raw data are available, how to extract useful information from such massive data has become the focus of people's attention. Data dimensionality reduction is an important research field in machine learning. Obtaining an efficient representation through appropriate dimensionality reduction techniques has become an important, necessary and challenging problem in multivariate data analysis. Dimensionality reduction should satisfy two basic properties: first, the size of the original data should be reduced; second, find and retain the principal...

AI Technical Summary

Problems solved by technology

[0008] The purpose of the present invention is to address the shortcomings of the above-mentioned prior art, and propose a data dimensionality reduction method based on neighborhood similarity, so as to effectively realize the feature extraction and reduction of data in the case of unbalanced data structure distribution. Dimensions, improve classification recognition effect

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