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Embedding manifold regression model based on Fisher criterion

A regression model and manifold technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as multicollinearity

Active Publication Date: 2015-03-25
TIANJIN UNIV
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Problems solved by technology

Huang et al. proposed an enhanced principal component regression model for face recognition, which can solve the problem of multicollinearity in linear regression models

Method used

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  • Embedding manifold regression model based on Fisher criterion
  • Embedding manifold regression model based on Fisher criterion
  • Embedding manifold regression model based on Fisher criterion

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Embodiment Construction

[0030] An embedded manifold regression model based on Fisher's criterion of the present invention will be described in detail below with reference to the embodiments and accompanying drawings.

[0031] In view of the fact that the current algorithm based on manifold learning tends to pay more attention to the local manifold structure, while ignoring the characteristics of discriminative information between categories, the present invention proposes an embedded manifold regression model based on Fisher criterion, the main idea of ​​which is the online Based on the regression model, make full use of the label information to construct an affinity graph between samples belonging to the same class and samples belonging to different classes to maintain the local manifold structure between samples of the same class, and cut off the samples belonging to different classes at the same time. The connection between samples with high similarity, maps all samples to the low-dimensional sub-m...

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Abstract

The invention provides an embedding manifold regression model based on a Fisher criterion. A method for the embedding manifold regression model comprises the following steps of performing initializing; expressing a training sample by using a matrix (img file= ' DDA0000627843280000011. TIF' wi= ' 581' he= ' 56' / ) under the condition that a category label corresponding to xm is that l (xm) belongs to {1, 2, ..., c}; preprocessing the training sample: mapping the training sample to principal component analysis subspace; establishing a similar matrix; separately processing a within-class sample and an inter-class sample by using the Fisher criterion; calculating embedding subspace: defining a Dxd mapping matrix W= [Omega1...Omega d] under the condition that d is dimensionality of the sample after the sample is subjected to feature conversion; and finding out mapping subspace by solving a feature vector (img file= ' DDA0000627843280000013. TIF' wi=' 123' he=' 55' / ) of a matrix (img file= ' DDA0000627843280000012. TIF' wi=' 205' he=' 58' / ). A conversion mode of the sample from the original higher-dimensional space to lower-dimensional manifold space is yi= WTx i=F (x i), and matrix representation of the conversion mode is Y=WTX=F(X), Y= [y(1), ..., Y(M)]. On the premise that label information of the sample is sufficiently used, local geometric structures of the same samples are maintained before and after dimensionality reduction, and the similarity of the samples which are in different categories but are high in similarity in the original space is reduced after dimensionality reduction.

Description

technical field [0001] The present invention relates to an embedded manifold regression model. In particular, it relates to an embedded manifold regression model based on Fisher's criterion, which improves traditional manifold learning and linear discriminant analysis by using label information and a linear regression model. Background technique [0002] Subspace learning and feature extraction are an important research direction in the field of machine vision and pattern recognition. The current common method is to find a mapping matrix to convert the features in the original input space into a low-dimensional subspace. The traditional method has principal components Analysis (Principal component analysis, PCA), independent component analysis (Independent component analysis, ICA) and other unsupervised learning methods that do not use label information; and linear discriminative analysis (Linear discriminative analysis, LDA) and other supervised learning that use training s...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
Inventor 冀中于云龙
Owner TIANJIN UNIV
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