Multi-modal data analysis method and system based on high Laplacian regularization and low-rank representation

Abstract

The invention discloses a multi-modal data analysis method and system based on high Laplacian regularization and low-rank representation and belongs to the field of multimodal data analysis. The invention aims to capture the global linear structure and nonlinear geometric structure of multimodal data. The method includes the following steps that: multi-modal data are processed, so that a pluralityof data matrices are obtained; low-rank representation and Laplacian regularization term are combined so as to construct a non-negative sparse hyper-laplacian regularization and low-rank representation model, the non-negative sparse hyper-laplacian regularization and low-rank representation model is made to learn each data matrix, so that a high Laplacian regularization and low-rank subspace is obtained; learning is performed on the basis of the high Laplacian regularization and low-rank subspace and a support vector machine, so that a plurality of classifiers can be obtained; and voting is performed for the classifiers, so that a final classifier is obtained. The structure of the system includes a data processing module, a data analysis module, a classification module, and a voting module. With the method adopted, the global linear structure and nonlinear geometry of the multimodal data can be captured.

Description

technical field [0001] The invention relates to the field of multimodal data analysis, in particular to a multimodal data analysis method and system with high Laplacian regularization and low rank representation. Background technique [0002] Low-rank representation techniques have attracted extensive attention due to their good effect on the study of low-dimensional subspace structures embedded in data. For a set of observation data, the goal of low-rank representation is to learn low-rank representation of observation data while separating noise data. Low-rank techniques have a wide range of applications in pattern recognition, computer vision, and signal processing. In the real world, data often exists on a low-dimensional manifold embedded in a high-dimensional environment space, and low-rank representation techniques only consider the global linear structure of the data without considering its nonlinear geometric structure, so in the learning process Locality and simi...

AI Technical Summary

Problems solved by technology

[0005] The technical task of the present invention is to address the above deficiencies, to provide a multimodal data analysis method and system with high Laplacian regularization and low rank representation, to solve how to capture the global linear structure and nonlinear geometric structure of multimodal data problem to better correctly classify multimodal data

Images