Embedding manifold regression model based on Fisher criterion

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...

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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

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