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An incomplete multi-view data clustering method and electronic equipment

A multi-view and complete technology, applied in the field of data analysis, can solve the problems of assigning weights to the importance of different views, the inability to accurately cluster multi-view data, and the lack of simultaneous utilization of global structural information and local structural information to achieve reliable clustering Results, the effect of improving clustering performance

Pending Publication Date: 2021-11-26
BEIJING UNIV OF POSTS & TELECOMM +1
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Problems solved by technology

[0003] The traditional clustering method for incomplete multi-view data does not complement the missing multi-view features, does not simultaneously utilize the global structural information and local structural information, and does not assign weights to the importance of different views, resulting in the inability to analyze the multi-view Accurate clustering of data

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  • An incomplete multi-view data clustering method and electronic equipment
  • An incomplete multi-view data clustering method and electronic equipment
  • An incomplete multi-view data clustering method and electronic equipment

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Embodiment Construction

[0018] In order to make the purpose, technical solutions and advantages of the present disclosure clearer, the present disclosure will be further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0019] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure shall have ordinary meanings understood by those skilled in the art to which the present disclosure belongs. "First", "second" and similar words used in the embodiments of the present disclosure do not indicate any sequence, quantity or importance, but are only used to distinguish different components. "Comprising" or "comprising" and similar words mean that the elements or items appearing before the word include the elements or items listed after the word and their equivalents, without excluding other elements or items.

[0020] As mentioned in the background technology s...

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Abstract

The invention provides an incomplete multi-view data clustering method and electronic equipment, and the method comprises the steps of complementing missing multi-view features of incomplete multi-view data through a multi-view auto-encoder, so as to obtain complete multi-view data and unified feature representation thereof; learning a local structure of the complete multi-view data through a single-layer neural network model, and extracting local structure information of the complete multi-view data by using a graph convolutional network to obtain node feature representation of each view of the complete multi-view data; and based on the unified feature representation and the node feature representation, performing clustering through a preset clustering algorithm to obtain a clustering result of the complete multi-view data. According to the technical scheme, after missing features of incomplete multi-view data are complemented, feature representation of the multi-view data is enhanced by combining the global structure and the local structure of the multi-view data, and then a more accurate clustering result of the multi-view data is obtained.

Description

technical field [0001] The present disclosure relates to the technical field of data analysis, and in particular to a clustering method and electronic equipment for incomplete multi-view data. Background technique [0002] The existing clustering methods for incomplete multi-view data generally use deep multi-view autoencoders to learn a unified data representation for data from multiple views, and establish a set of multi-view autoencoders for the features of each view. Including the encoder part and the decoder part. For incomplete multi-view data, a weighted fusion method is used to fuse the outputs of each view encoder and represent them in a unified manner. At the same time, graph embedding constraints are added to the unified representation learning process so that the learned representation can retain local structural information. In addition, a clustering loss function is added after the unified representation layer to cluster multi-view data. [0003] The traditio...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/23213G06F18/214
Inventor 薛哲杜军平宋杰郑长伟梁美玉
Owner BEIJING UNIV OF POSTS & TELECOMM
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