Multi-modal robust feature learning model based on non-negative matrix factorization

Abstract

The invention discloses a multi-modal robust feature learning model based on non-negative matrix factorization, and belongs to the technical field of computers. The model comprises the following stepsof firstly, carrying out normalization and special value preprocessing on a multi-modal data set; secondly, reconstructing modal data in a low-dimensional shared space, simulating a geometric space in a data space by utilizing a graph regularization thought, introducing a noise matrix to remove noise in the data space, and constructing a multi-modal robust feature learning model based on non-negative matrix factorization; thirdly, according to a model optimization result, sequentially updating the mapping matrix of each mode and the shared feature matrix of all modes, updating the noise matrix and updating a modal weight factor; and finally, judging the difference between the current model value and the last model value, and iteratively updating the third step until a model convergence condition is met. An effective model is derived according to the steps to solve the feature learning problem of the multi-modal data containing noise. A large number of experiments prove that the data representation performance obtained by the method is superior to that of the related model at the present stage.

Description

Technical field [0001] The invention belongs to the field of computer technology, and relates to a multi-modal robust feature learning model based on non-negative matrix factorization, and a multi-modal non-negative correlation feature learning model that refers to regularization of graphs in data space and noise feature space. Background technique [0002] In real life, people are accustomed to extracting information from the characteristics of objects, and this information is expressed as massive amounts of data in computer language. How to use these massive data to express and correlate the diverse characteristics of real things is a question worth exploring. Multimodal analysis provides a way to solve this problem. It uses data sets under different modalities to describe the basic characteristics of the same thing on different sides. Through the learning of the common subspace expressed by these different basic characteristics, it helps us to build the connection between the...

AI Technical Summary

Problems solved by technology

Use the idea of ​​graph regularization to realize the fitting of the geometric structure of the original data space; add a weight factor for each mode, set the model so that the mode weight is adaptively updated to control the influence of each mode on the common subspace; at the same time, for To solve the adverse effect of data set noise on multimodal fusion features, we introduce noise matrix into the model to reduce the impact of noise on common subspace feature learning

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