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Maximum correlation principal component analysis method based on deep parameter learning

A principal component analysis and depth parameter technology, applied in the field of data processing, can solve problems such as multiple storage spaces and computing costs, hinder analysis of data correlation, and increase the difficulty of data analysis

Active Publication Date: 2018-11-02
BEIJING UNIV OF TECH
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Problems solved by technology

High dimensions not only require more storage space and computing costs, but also increase the difficulty of data analysis due to the "curse" of dimensionality
Second, real-world data is likely to be corrupted by various noises, which hinders the analysis of real information and existing correlations in the data.
However, finding transfer operators is a difficult task

Method used

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  • Maximum correlation principal component analysis method based on deep parameter learning
  • Maximum correlation principal component analysis method based on deep parameter learning
  • Maximum correlation principal component analysis method based on deep parameter learning

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Embodiment Construction

[0012] This method of maximum correlation principal component analysis based on deep parameter learning uses deep parameterization to approach unknown but existing nonlinear functions, maps high-dimensional data with nonlinear structures to data of the same dimension with linear structures, and then uses Principal component analysis reduces the dimensionality of the data.

[0013] The present invention parameterizes the transfer operator through learning. The method uses independent mapping chains for each feature, which is similar to the forward propagation structure of the neural network, but the interaction between variables is independent. Therefore, each mapping chain has only one input variable and approximates a non-linear transition operator. This method uses the deep parameterization method to approximate the unknown but existing nonlinear function, maps the high-dimensional data with a nonlinear structure to the same-dimensional data with a linear structure, and then...

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Abstract

The invention discloses a maximum correlation principal component analysis method based on deep parameter learning. The effective dimension reduction can be carried out for high-dimensional data witha nonlinear structure. According to the maximum correlation principal component analysis method based on the deep parameter learning, through utilization of a deep parameterization method, an unknownexisting nonlinear function is approached, the high-dimensional data with the nonlinear structure is mapped to same-dimensional data with the nonlinear structure, and the dimension reduction is carried out on the data through principal component analysis.

Description

technical field [0001] The invention belongs to data processing, especially the technical field of data dimensionality reduction of face database, and in particular relates to a maximum correlation principal component analysis method based on deep parameter learning. Background technique [0002] In the era of big data, we are faced with more and more data analysis and data processing tasks. There are two main problems when faced with these tasks. One is that in the real world, data in higher dimensional forms are usually obtained. These data generally embed the intrinsic low-dimensional structure hidden in the low-dimensional subspace or manifold in the high-dimensional data space. High dimensions not only require more storage space and computational costs, but also increase the difficulty of data analysis due to the "curse" of dimensionality. Second, real-world data is likely to be corrupted by various noises, which hinders the analysis of real information and existing ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/21355
Inventor 孙艳丰陈浩然胡永利
Owner BEIJING UNIV OF TECH
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