Data fusion-oriented iterative structured multi-view subspace clustering method and device and readable storage medium

Abstract

The invention provides a data fusion-oriented iterative structured multi-view subspace clustering method and device and a readable storage medium. The method comprises the steps of constructing a multi-view subspace clustering ISSMSC model; carrying out solving and target optimization on the target function, obtaining the number k and dimensions of subspaces, segmenting data points into the subspaces, and achieving multi-view subspace clustering. According to the matrix of the method, the relationship between different clusters is reduced, and the relationship in the same cluster is enhanced.Comparison of adjacency matrices demonstrates the advantages of the model. According to the method, based on the self-expression characteristic of the data, the shared information among the views is explored, and the potential supplementary information among the views is utilized. In consideration of the influence of a segmentation matrix generated in the clustering process on adjacent matrix learning, a structured l1 norm is introduced in the learning process. In addition, an effective optimization algorithm is designed to solve the problem.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a data fusion-oriented iterative structured multi-view subspace clustering method, a device and a readable storage medium. Background technique [0002] With the development of information technology, a large amount of data is generated every day. It is no exaggeration to say that we live in an ocean of data, most of which are high-dimensional. Due to the limitations of computer performance, dealing with high-dimensional data is not an easy task. In the calculation process, as the amount of data increases, the amount of calculation increases exponentially. This phenomenon is often referred to as the curse of dimensionality. To avoid this effect, there are many dimensionality reduction strategies, such as Principal Component Analysis (PCA), Nonnegative Matrix Factorization (NMF), Linear Discriminant Analysis (LDA), etc. These methods have been widely used in many fiel...

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Problems solved by technology

Studies have shown that the learning of the adjacency matrix will be affected by the quality of the data, and the original data usually contains noise and redundant information, so it is difficult to directly use the original data for reconstruction to meet the actual needs, and the accuracy of the image is low.

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