A Generalization Algorithm for Manifold Learning Based on Local Linear Regression

Abstract

The invention discloses a manifold learning generic algorithm based on local linear regression and belongs to the technical field of data dimension reduction of hyperspectral images. The manifold learning generic algorithm based on local linear regression is suitable for any manifold learning algorithms and can keep original manifold learning dimension reduction results. The method includes the first step of searching for a neighborhood, the second step of calculating a projection matrix, the third step of obtaining a linear regression coefficient matrix and the fourth step of calculating a dimension reduction result of a new sample. The generalization of the new sample is achieved on the basis of keeping the original manifold learning dimension reduction results, linear mapping from high dimension to low dimension is established, any manifold learning algorithms such as LE, LLE and LTSA having no generalization ability can be made to have the generalization ability, and therefore the time-consuming manifold learning algorithms are applicable to the dimension reduction process of the hyperspectral images.

Description

technical field [0001] The invention belongs to the technical field of hyperspectral image data dimensionality reduction, and in particular relates to a generalization algorithm for manifold learning. Background technique [0002] Hyperspectral images can record rich spectral information of ground objects, which is conducive to accurate and fine classification and identification of ground objects. However, the increase in the number of hyperspectral data bands will inevitably cause information redundancy and data processing difficulties, and bring about the disaster of dimensionality. This phenomenon hinders hyperspectral data processing. Redundancy has become a problem that must be solved. The redundancy of hyperspectral data is mainly caused by the correlation between the bands of hyperspectral data. Dimensionality reduction is an important preprocessing method, which uses low-dimensional data to express the characteristics of high-dimensional data, and it can effectively...

AI Technical Summary

Problems solved by technology

However, most of the classical nonlinear manifold learning algorithms have no generalization ability and cannot directly learn new hyperspectral data. They must learn together with all the old data to obtain new dimensionality reduction results.

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