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Local subspace learning-based multi-view clustering method

A technology of subspace learning and clustering method, which is applied in the field of multi-view clustering based on local subspace learning, and can solve the problem that multiple features cannot be used at the same time.

Inactive Publication Date: 2016-11-09
DALIAN UNIV OF TECH
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Problems solved by technology

[0004] The embodiment of the present invention provides a multi-view clustering method based on local subspace learning to overcome the problem that multiple features cannot be used simultaneously for clustering in the prior art

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Embodiment Construction

[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0020] In terms of image clustering, different feature extraction algorithms can be used for image feature extraction. For example, four algorithms of LBP, MSD, CDH, and MTH are used for feature extraction of image data sets, and the obtained results are recorded as Use the Gaussian kernel function on the features extracted from different viewing...

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Abstract

The invention discloses a local subspace learning-based multi-view clustering method. The method comprises the steps of performing characteristic extraction on data in different views and generating a corresponding kernel matrix; obtaining a unified kernel matrix by using a local subspace learning-based multi-view method; performing mapping by using a Laplace mapping algorithm; and performing clustering on expressions in a low-dimensional space by adopting a Kmeans algorithm to obtain a result. The multi-view clustering method is realized and the clustering effect is improved.

Description

technical field [0001] The invention relates to the technical field of data analysis, in particular to a multi-view clustering method based on local subspace learning. Background technique [0002] K-means algorithm is one of the most widely used clustering algorithms at present. The K-means algorithm is suitable for the case where a single feature is distributed and the shape of the cluster is convex. Its basic idea is to randomly specify the initial center, and divide all samples into the nearest center according to the distance from the center point, and iteratively update the value of the cluster center until the algorithm converges. [0003] Therefore, it is difficult for the Kmeans algorithm to cluster using different features of the same data at the same time, and the effect is not ideal when the shape of the data distribution is not convex. Contents of the invention [0004] An embodiment of the present invention provides a multi-view clustering method based on l...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/23213
Inventor 冯林刘胜蓝刘洋
Owner DALIAN UNIV OF TECH
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