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Redundant source synergistic reducing method of multi-source heterogeneous big data

A multi-source heterogeneous, big data technology, applied in the information field

Inactive Publication Date: 2016-08-10
INST OF INFORMATION ENG CAS
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Problems solved by technology

[0018] Table 2. Insufficiency of existing multi-source data sample selection methods

Method used

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  • Redundant source synergistic reducing method of multi-source heterogeneous big data
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  • Redundant source synergistic reducing method of multi-source heterogeneous big data

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Embodiment Construction

[0078] The present invention will be further described below through specific examples.

[0079] The redundant source cooperative reduction method for multi-source heterogeneous big data provided by the present invention is composed of heterogeneous manifold smooth learning HMSL and correlation-based multi-source redundant reduction CMRR algorithm, and the gradual optimization of the model is realized through a cyclic iterative process.

[0080] The HMSL model in formula (7) can be simplified as:

[0081]

[0082] Among them, F(·)=f s (·)+αg M (·)-βh D (·) is a smooth objective function, Z=[A Z B Z W Z m Z ] represent optimization variables, is a closed convex set with respect to a single variable:

[0083]

[0084] Since F( ) is a continuously differentiable function with respect to Lipschitz's continuous gradient L (reference: Y.Nesterov.Introductory lectures on convex optimization, volume 87. SpringerScience & Business Media, 2004.):

[0085]

[0086] ...

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Abstract

The invention relates to a redundant source synergistic reducing method of multi-source heterogeneous big data. The method comprises two models: an HMSL (Heterogeneous Manifold Smoothness Learning) model and a CMRR (Correlation-based Multi-view Redundancy Reduction) model, wherein the HMSL model is used for linearly projecting multi-source heterogeneous data onto a low-dimension characteristic isomorphic space and enabling the manifold distance of information correlated descriptions and the Euclidean distance of semantic complementary samples to be shorter in the space; and the CMRR model is used for eliminating three-way redundancies and double-level heterogeneities of multi-source redundant data by utilizing gradient energy competition strategy-based generalized elementary transformation constraint in the characteristic isomorphic space obtained through HMSL model learning. The method disclosed by the invention has the advantages that the three-way redundancies and the double-level heterogeneities of the multi-source redundant data can be eliminated and redundant sources of the multi-source heterogeneous data are simplified.

Description

technical field [0001] The invention belongs to the field of information technology, and aims at the problems of three-way redundancy and double-layer heterogeneity in a massive multi-source heterogeneous redundant data environment, and proposes a redundant source collaborative reduction method for multi-source heterogeneous big data. Background technique [0002] In recent years, with the emergence of a large number of high-tech digital products, the multi-source heterogeneous data (Multi-source Heterogeneous Data) generated by these heterogeneous electronic devices has spread to every corner of people's real life. The so-called multi-source heterogeneous data refers to data that comes from different sources or channels, but expresses similar content, and appears in various styles such as different forms, different modalities, different perspectives, and different backgrounds. For example, Sina Weibo, Tencent WeChat, and Sohu.com report different forms of the same news; the...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/215
Inventor 张磊王树鹏云晓春
Owner INST OF INFORMATION ENG CAS
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