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

An iterative structure, data-oriented technology, applied in the field of image processing, can solve the problems of low image accuracy and difficult to meet actual needs, etc.

Inactive Publication Date: 2020-09-01
SHANDONG UNIV OF FINANCE & ECONOMICS +1
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  • Abstract
  • Description
  • Claims
  • Application Information

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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 r

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

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Embodiment Construction

[0025] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the relationship between hardware and software Interchangeability. In the above description, the composition and steps of each example have been generally described according to their functions. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the present invention.

[0026] The block diagrams shown in the drawings are merely functional entities and do not necessarily correspond to physically separate entiti...

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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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Application Information

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IPC IPC(8): G06F16/28G06F16/26
CPCG06F16/26G06F16/285
Inventor 于晓刘慧郭强阮怀军封文杰
Owner SHANDONG UNIV OF FINANCE & ECONOMICS
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