Tensor model-based multi-source data classification optimizing method and system

A multi-source data, classification optimization technology, applied in the field of pattern recognition, can solve the problems of low computational efficiency and high computational complexity, and achieve the effect of ensuring high efficiency, improving classification accuracy, and avoiding over-learning

Inactive Publication Date: 2016-08-31
SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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[0005] The present invention provides a multi-source data classification optimization method and system based on tensor patterns, aiming to solve the problem of high computational c

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  • Tensor model-based multi-source data classification optimizing method and system
  • Tensor model-based multi-source data classification optimizing method and system
  • Tensor model-based multi-source data classification optimizing method and system

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[0049] In order to make the objectives, technical solutions and advantages of the present invention clearer, the following further describes the present invention in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, but not to limit the present invention.

[0050] See figure 1 , Is a flowchart of a method for multi-source data classification optimization based on tensor mode in an embodiment of the present invention. The multi-source data classification optimization method based on tensor mode in the embodiment of the present invention includes the following steps:

[0051] Step 100: Introduce the multi-view data into a unified tensor product space, and perform tensor product operations on the multi-view data under the Map-reduce distributed framework to obtain high-order tensor data;

[0052] In step 100, Map-Reduce is a parallel distributed computi...

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Abstract

The invention relates to a tensor model-based multi-source data classification optimizing method and system. The tensor model-based multi-source data classification optimizing method comprises the following steps: in step a, under a Map-reduce distribution framework, multi-view data is subjected to tensor product operation, high order tensor data is obtained, and an initial support tensor machine classification model is built according to the high order tensor data; in step b, the data of all views is subjected to feature elimination operation via a support vector recursive feature elimination algorithm in an original space, and subscript data where the data of all views retains features is output; in step c, according to the subscript data where the data of all views retains features, parameters of the initial support tensor machine classification model are optimized, and a final support tensor machine classification model is determined; in step d, test samples are input to the support tensor machine classification model and are classified. Via use of the tensor model-based multi-source data classification optimizing method and system, classification accuracy of the classification model can be effectively improved, calculation complexity can be lowered, that redundant information in tensor data can be identified by the classification model is ensured, and classifying speed of the classification model can be further improved.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, and in particular relates to a tensor pattern-based multi-source data classification optimization method and system. Background technique [0002] Pattern Recognition (Pattern Recognition) is to study the automatic processing and interpretation of patterns by computer using mathematical techniques. We refer to environments and objects collectively as "patterns". With the development of computer technology, it is possible for humans to study complex information processing processes. An important form of information processing is the identification of living organisms to the environment and objects. Of particular importance to humans is the recognition of optical information (obtained through the organs of vision) and acoustic information (obtained through the organs of hearing), which are two important aspects of pattern recognition. [0003] With the continuous development of compu...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/2411
Inventor 王书强刘志华胡勇郭毅可曾德威卢哲
Owner SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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