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A tensor-based cross-domain heterogeneous large data multi-view clustering method and apparatus

A multi-view, big data technology, applied in the field of information processing, can solve problems such as inability to generate, achieve effective distance, improve the impact of clustering results, and have good interpretability.

Inactive Publication Date: 2019-01-18
EZHOU INST OF IND TECH HUAZHONG UNIV OF SCI & TECH +1
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0005] The embodiment of the present invention provides a tensor-based cross-domain heterogeneous big data multi-view clustering method and device, which solves the problem that different clustering results cannot be generated according to different scenarios in the prior art to provide high-quality clustering for upper-level big data applications. Technical issues with class services

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  • A tensor-based cross-domain heterogeneous large data multi-view clustering method and apparatus
  • A tensor-based cross-domain heterogeneous large data multi-view clustering method and apparatus
  • A tensor-based cross-domain heterogeneous large data multi-view clustering method and apparatus

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Embodiment 1

[0045] figure 1 It is a schematic flowchart of a tensor-based cross-domain heterogeneous big data multi-view clustering method in an embodiment of the present invention. Such as figure 1 As shown, the method includes:

[0046] Step 110: Construct a sample tensor according to the fused cross-domain heterogeneous feature space, and construct a feature space combination vector according to different contexts.

[0047] Further, the cross-domain heterogeneous feature space includes one or more of cyberspace, physical space and social space.

[0048] Specifically, please refer to Figure 4 , constructing a sample tensor based on the fused cross-domain heterogeneous feature space where F 1 ,F 2 ,...,F l Represents l feature spaces. In view of the high-dimensional, multi-source, and heterogeneous characteristics of big data, tensor fusion is used to mine the internal structure of cross-domain heterogeneous multi-source information, which can simultaneously consider the impact...

Embodiment 2

[0063] Based on the same inventive concept as the tensor-based cross-domain heterogeneous big data multi-view clustering method in the foregoing embodiments, the present invention also provides a tensor-based cross-domain heterogeneous big data multi-view clustering device, Such as figure 2 As shown, the device includes:

[0064] A first construction unit, the first construction unit is used to construct a sample tensor according to the fusion cross-domain heterogeneous feature space, and construct a feature space combination vector according to different contexts;

[0065] A first obtaining unit, the first obtaining unit is used to accumulate the sample tensor to obtain a merged tensor;

[0066] A second obtaining unit, the second obtaining unit is used to perform normalization along the order corresponding to each feature space of the merged tensor to obtain a connection tensor;

[0067] A third obtaining unit, the third obtaining unit is used to calculate a stationary di...

Embodiment 3

[0085] Based on the same inventive concept as the tensor-based cross-domain heterogeneous big data multi-view clustering method in the foregoing embodiments, the present invention also provides a computer-readable storage medium on which a computer program is stored, and the program is When the processor executes, it implements the steps of any one of the aforementioned tensor-based cross-domain heterogeneous big data multi-view clustering methods.

[0086] Among them, in Figure 5 In, bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 will include one or more processors represented by processor 302 and various types of memory represented by memory 304 circuits linked together. The bus 300 may also link together various other circuits, such as peripherals, voltage regulators, and power management circuits, etc., which are well known in the art and thus will not be further described herein. The bus interface ...

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Abstract

The invention provides a cross-domain heterogeneous large data multi-view clustering method and device based on tensor. The method includes: constructing sample tensor according to fused cross-domainheterogeneous feature space, and constructing feature space combination vector according to different context; accumulating the sample tensor to obtain a combined tensor; normalizing the order corresponding to each characteristic space of the merging tensor to obtain a connection tensor; obtaining the feature space scoring vectors according to the connection tensor, and obtaining a scoring tensorby outer product of the feature space scoring vectors; introducing the feature space combination vector and the scoring tensor into a high-dimensional space tensor distance to construct a combined scoring tensor distance; calculating sample similarity according to the combined scoring tensor distance, and constructing a view matrix according to the feature space combination vector; obtaining the multi-view clustering results under different views according to the view matrix. The invention solves the technical problem that in the prior art, different clustering results cannot be generated according to the requirements of different applications in different contexts, thereby providing high-quality clustering services for different applications.

Description

technical field [0001] The present invention relates to the technical field of information processing, in particular to a tensor-based multi-view clustering method and device for cross-domain heterogeneous big data. Background technique [0002] Multi-view clustering analysis is an emerging research field of data mining. It can explore unknown data sets from different perspectives. There are multiple clustering processes, allowing one or more clustering results. Compared with the traditional single clustering, which explores an unknown data set from one perspective and only produces one clustering result, it is more in line with the diversity of human beings' view of the world. Therefore, multi-view clustering analysis of big data can uncover all the structures in the data and serve humans better. [0003] Existing multi-view clustering techniques mainly include multi-view clustering, selection clustering and subspace clustering. Multi-view clustering can integrate the int...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/23G06F18/22
Inventor 杨天若赵雅靓张荣皓
Owner EZHOU INST OF IND TECH HUAZHONG UNIV OF SCI & TECH
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