Data dimension reduction method for improved neighborhood preserving embedding algorithm

Abstract

The invention discloses a data dimension reduction method for an improved neighborhood preserving embedding algorithm. The method comprises the steps of firstly, constructing an adjacency graph, and calculating out a near point of each sample point by using a geodesic line, thereby forming an adjacency matrix; secondly, calculating a reconstruction weight value, and representing each sampling point with the near point; and finally, calculating a projection matrix, and performing calculation by utilizing a reconstruction weight value matrix to obtain a transform projection matrix. According tothe method, a Euclidean distance is replaced with a geodesic line distance; local structure information of the NPE algorithm is better kept; and the manifold structure processing capability of the algorithm is improved.

Description

technical field [0001] The invention belongs to the field of big data processing, and relates to a data dimensionality reduction method, in particular to a data dimensionality reduction method with a neighborhood-preserving embedded improved algorithm. Background technique [0002] In the era of big data, the continuous expansion of data volume has led to an information explosion. These data often exhibit high-dimensional characteristics. Because of the complexity of its structure, high-dimensional data is usually difficult to directly process in the real world. For example, the main purpose of data mining is to use efficient algorithms to explore the information hidden behind the data, and finally transform it into knowledge to guide people to make reasonable decisions. In order to properly deal with these high-dimensional data, data dimensionality reduction technology was born. Data dimensionality reduction is the process of projecting data from a high-dimensional feature...

AI Technical Summary

Problems solved by technology

In order to effectively explore the nonlinear structure contained in the data set, people have developed many effective nonlinear dimensionality reduction methods, such as artificial neural network, genetic algorithm, manifold learning, etc. for nonlinear dimensionality reduction algorithms. Linear algorithms perform well on training samples, but cannot achieve dimensionality reduction for test samples because they lack projection matrices and cannot perform feature extraction on newly added sample sets. In order to solve this problem, the linearized typical manifold learning algorithm is Proposed, such as the manifold learning (NPE) of the neighborhood-preserving embedding algorithm, using local representations to obtain projection matrices, and projecting high-dimensional manifold data to low-dimensional manifold spaces

However, such local representations usually assume that the local manifold space is linear, which will lead to large fluctuations in dimensionality reduction results.

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