Missing multi-view data classification method and system

Abstract

The invention discloses a missing multi-view data classification method and system. The method comprises the following steps: reconstructing a first implicit space by missing multi-view training sample data, and reconstructing a second implicit space by missing multi-view sample data to be tested; training sample data and a reconstruction loss function by the first implicit space and the missing multi-view; training a multi-view multi-path neural network model, inputting the first implicit space and the real class label into the trained model, and adjusting the first implicit space by taking the total loss function as a target function until the reconstruction loss function and the total loss function are converged to obtain a trained model and a first complete implicit space; inputting the second implicit space into the trained model, and adjusting the second implicit space by taking the reconstruction loss function as a target function to obtain a second complete implicit space; andclassifying the missing multi-view sample to be tested according to the first complete hidden space and the second complete hidden space. According to the invention, the accuracy of classifying missing multi-view data can be improved.

Description

technical field [0001] The invention relates to the technical field of image classification, in particular to a classification method and system for missing multi-view data. Background technique [0002] Multi-view data is very common in life, for example, magnetic resonance imaging and computed tomography in the medical field. These different types of data contain complementary information that is effective for disease diagnosis. However, it is difficult to integrate multiple types of data and make full use of them. In addition, the lack of partial view data makes modeling more difficult. [0003] Although the field of multi-view learning has developed rapidly in recent years, it is still limited by the effective modeling of complex relationships, and it is difficult for existing technologies to effectively solve the situation of missing views. In dealing with the problem of missing views, some technologies discard missing data and only keep complete data, which will lose ...

AI Technical Summary

Problems solved by technology

These different types of data contain complementary information that is effective for disease diagnosis. However, it is difficult to integrate multiple types of data and make full use of them. In addition, the lack of partial view data makes modeling more difficult.

[0003] Although the field of multi-view learning has developed rapidly in recent years, it is still limited by the effective modeling of complex relationships, and it is difficult for existing technologies to effectively solve the situation of missing views.

In dealing with the problem of missing views, some technologies discard missing data and only keep complete data, which will lose a lot of data information, especially when the sample size is scarce; some technologies are grouped according to the missing data, and each group is trained independently. It will not be able to fully mine the relationship between the data, and it will also lead to complex grouping when the missing situation is diverse

This leads to the fact that the existing classification methods for missing multi-view data cannot balance the consistency relationship and information complementarity between multi-view data, resulting in low classification accuracy.

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