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Nonlinear characteristic extraction and classification based on defect data

A technology with nonlinear features and missing data. It is used in character and pattern recognition, instruments, computer parts, etc., and can solve problems such as the effect of manifold learning algorithms.

Active Publication Date: 2016-12-07
HUAQIAO UNIVERSITY
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Problems solved by technology

Although manifold learning has the advantages of fewer parameters, simple implementation, and fast calculation, it requires the data to have an approximately complete manifold structure, so when the data is incomplete, the effect of the manifold learning algorithm will be greatly affected

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  • Nonlinear characteristic extraction and classification based on defect data
  • Nonlinear characteristic extraction and classification based on defect data
  • Nonlinear characteristic extraction and classification based on defect data

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Embodiment Construction

[0061] see figure 1 , a kind of non-linear feature extraction and classification method based on defective data provided by the present invention, comprises the following steps:

[0062] Step 101: Using a distance estimation method for missing data to construct a local neighborhood set of data points and a missing marker set corresponding to the local neighborhood set.

[0063] For each data point x i (i=1,2,…,n), define a missing marker vector f i =(f i1 ,f i2 ,..., f im ) T , where f it = 0 if and only if data point x i The tth attribute value in is missing, otherwise f it =1, then the entire data set X=(x 1 ,x 2 ,…,n) the missing marker matrix is ​​F=(f 1 ,f 2 ,..., f n ). The present invention is based on the basic idea of ​​cosine similarity, regards data points as their corresponding vectors, then two data points x with missing values i and x j The distance between can be expressed as

[0064] s i m ( ...

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Abstract

The present invention discloses a nonlinear characteristic extraction and classification based on defect data. The method comprises a step of using the distance estimation method of defect data to construct the local neighborhood set of a data point and a missing marker set corresponding to the local neighborhood set, a step of constructing a nuclear norm regularization model based on the local neighborhood set and the corresponding missing marker set, and using an adaptive fixed point iteration algorithm to solve the model to extract a local coordinate, and a step of aligning the local coordinate to obtain a global coordinate. According to the method, in facing the defect data, the local neighborhood set can be constructed, the local coordinate can be extracted, the global coordinate can be covered, and the nonlinear characteristic extraction and classification of the defect data are finally realized.

Description

technical field [0001] The invention relates to the direction of data mining in information processing technology, can be applied to the field of nonlinear feature mining of missing data, and particularly relates to a nonlinear feature extraction and classification method based on missing data. Background technique [0002] The progress of science, especially the development of the information industry, has brought us into a new information age. In the process of scientific research in the information age, it is inevitable to encounter a large amount of data, such as global climate models, image classification systems, text clustering, and gene sequence modeling. Although people can obtain a large amount of information resources, due to the lack of means to mine the hidden knowledge behind the data, people cannot better discover the relationships and rules existing in the data, nor can they predict future development based on the existing massive data. trend. At present, d...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/241
Inventor 王靖孙晓龙杜吉祥钟必能
Owner HUAQIAO UNIVERSITY
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