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Robust canonical correlation analysis algorithm based on generalized mean value

A technology of canonical correlation analysis and mean value, applied in computing, computer parts, instruments, etc., can solve problems such as non-robust high-dimensional samples for outliers

Active Publication Date: 2016-10-12
航遨航空科技(西安)有限公司
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Problems solved by technology

[0004] In order to solve the problem of non-robustness to outliers and high-dimensional small samples commonly existing in typical correlation algorithms such as traditional CCA, KCCA and LPCCA, the present invention proposes a robust canonical correlation analysis algorithm based on generalized mean (CCA based ongeneralized mean, GMCCA)

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  • Robust canonical correlation analysis algorithm based on generalized mean value
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  • Robust canonical correlation analysis algorithm based on generalized mean value

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Embodiment Construction

[0067] In order to clarify the purpose, technical solutions and advantages of the present invention, the present invention will be further described in detail below in conjunction with specific embodiments and accompanying drawings.

[0068] refer to figure 1 , the specific implementation process of the present invention comprises the following steps:

[0069] (1) Input a set of sample sets of size N The parameter p of the generalized mean, the total number of internal iterations T 1 and T 2 , the total number of external iterations T, the dimension of the feature after dimensionality reduction d;

[0070] (2) First, the centralized sample set X=(x 1 ,x 2 ,...,x N ) and Y=(y 1 ,y 2 ,...,y N ):

[0071] Calculation sample set X=(x 1 ,x 2 ,...,x N ) and Y=(y 1 ,y 2 ,...,y N ) center value:

[0072] x ‾ = 1 N Σ i = ...

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Abstract

The invention discloses a robust canonical correlation analysis algorithm based on a generalized mean value, and mainly solves problems that a conventional canonical correlation analysis algorithm is not robust for outliers and a high-dimensional small sample causes a singular problem of a sample covariance. The implementation process comprises the steps: (1), inputting necessary parameters, and carrying out the centralized processing of a training sample; (2), solving two groups of projection sets of conventional canonical correlation analysis; (3), reconstructing a target optimization function of a model based on a generalized mean value, so as to inhibit the impact on a target function from the outliers; (4), solving the target function through employing a linear iteration method, wherein the two groups of projection sets of conventional canonical correlation analysis are used for initialization; (5), enabling the solved two groups of projection sets of the conventional canonical correlation analysis based on the generalized mean value to be used for the feature extraction and dimension reduction of the sample. The experiment results on a multi-feature handwritten form database (MFD), a human face database (ORL) and an object image database (COIL-20) verify the effectiveness of the algorithm.

Description

technical field [0001] The invention belongs to the technical field of feature extraction and data dimension reduction, and mainly relates to the improvement and optimization of typical correlation analysis algorithms. Specifically, it is a robust canonical correlation analysis algorithm based on generalized mean, which can be applied in fields such as machine learning, pattern recognition, data mining and image processing. Background technique [0002] In the field of pattern recognition and machine learning, dimensionality reduction (DR) has always been one of the hot research topics. A large number of methods have been proposed for data dimensionality reduction, among which Principal Component Analysis (PCA) is one of the most classic methods. But PCA focuses on feature extraction and dimensionality reduction of single-mode data (Single-viewdata). With the development of science and technology, the means for people to collect data are more diversified, and the same thin...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/22
Inventor 葛洪伟顾高升李莉朱嘉钢
Owner 航遨航空科技(西安)有限公司
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