Multi-source heterogeneous data fusion method based on deep subspace switching ensemble learning

Abstract

The invention discloses a multi-source heterogeneous data fusion method based on deep subspace switching integrated learning, which is carried out according to the following steps: S1, respectively extracting features of each source data set and composing spatial spectrum features; S2, carrying out recombination to form a spatial spectrum characteristic data set; S3, processing the sample data inthe spatial spectrum characteristic data set based on a deep sample learn algorithm to form an original sample, a first-order sample and a second-order sample; S4, respectively constructing a classifier model for the three groups of sample sets; S5, classifying the three groups of models by appropriate classification methods to obtain three groups of classification results, and then getting the final classification results by classification voting. The invention combines spectral space information and incorporates layered subspace switching ensemble learning algorithm, utilizes multi-scale spatial spectrum samples, increases useful samples, improves training quality, retains edge information, and improves diversity and separability of samples.

Description

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AI Technical Summary

Benefits of technology

This patented technology uses multiple scale space data from different sources combined with an ensemble method for better understanding complex systems such as biological networks or gene expression patterns that are important factors affecting their performance. It can improve both accuracy and efficiency by combining these techniques together without sacrificing any specific details about each other's components.

Problems solved by technology

This patented technical problem addressed in this patents relates to improving traditional methodologies used for analyzing spectra that rely on pixels' spectral content alone but neglects important space related characteristics like location and depth within each frame (spatial structures).

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