Deep multi-view clustering method, system and equipment based on common difference learning
A multi-view and deep technology, applied in the field of data processing, can solve the problem of poor adaptability of high-quality view features of clustering effect, and achieve the effect of improving clustering effect and adaptability and reducing redundant information.
Inactive Publication Date: 2022-07-12
湖南工商大学
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[0004] In view of this, the embodiments of the present disclosure provide a deep multi-view clustering method, system and device based on common difference learning, which at least partially solves the problems of existing clustering effects and poor adaptability of using high-quality view features in the prior art. question
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[0042] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.
[0043] The embodiments of the present disclosure are described below through specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only some, but not all, embodiments of the present disclosure. The present disclosure can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments may be combined with each other under the condition of no conflict. Based on the embodiments in the present disclosure, all ot...
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The embodiment of the invention provides a depth multi-view clustering method, system and device based on common difference learning, and belongs to the technical field of data processing, and the method specifically comprises the steps: building a common difference depth multi-view feature learning network; respectively connecting each view of the multi-view data with a common information extraction network and a difference information extraction network; inputting the common information extraction network of all views of the multi-view data into a common information learning module for training until convergence; inputting the common information extraction network and the difference information extraction network of all views of the multi-view data into a difference information learning module, and obtaining complementarity features of each view of the multi-view data through orthogonal constraints; the consistency features and all complementarity features are connected in series to form multi-view fusion features; and inputting the multi-view fusion features into a clustering model based on KL divergence for clustering. Through the scheme of the invention, the clustering effect and adaptability under the condition of initial extremely serious imbalance of the multi-view data are improved.
Description
technical field [0001] The embodiments of the present disclosure relate to the technical field of data processing, and in particular, to a deep multi-view clustering method, system and device based on shared difference learning. Background technique [0002] At present, the basic idea of clustering is to divide the samples in the dataset into several clusters according to the similarity between them. Traditional clustering algorithms are mainly for single-view data, and the data has only one set of features. When the data has multiple sets of features, it is called multi-view data. Multi-view data not only contains more rich and useful information, but also brings redundant information between different views. At present, most multi-view clustering mainly focuses on maximizing the common information of multi-views, ignoring the difference information of each view, that is, the complementary information of multi-view data is not fully mined; the initial multi-view data is...
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IPC IPC(8): G06K9/62G06N3/04G06N3/08G06V10/762G06V10/764G06V10/80G06V10/82
CPCG06N3/08G06N3/045G06N3/048G06F18/23G06F18/24G06F18/253
Inventor 李晓翠张新玉史庆宇
Owner 湖南工商大学