Manifold learning-based data dimensionality-reduction method and device

A technology of data dimensionality reduction and manifold learning, applied in image data processing, graphics and image conversion, instruments, etc., can solve the problems of consuming computing resources, reducing the running speed of algorithms, and reducing system performance

Active Publication Date: 2016-06-15
GUANGZHOU HONGSEN TECH
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Problems solved by technology

[0004] First of all, in the previous LPP algorithm, the entire face image was considered as a whole, but recent studies have shown that changes in the face due to factors such as lighting conditions and facial expressions are often only reflected in some areas of the image. That is, there is a situation where local data is scattered, while other parts have little or no change. Therefore, if the entire face image is taken as a whole in the LPP algorith

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  • Manifold learning-based data dimensionality-reduction method and device
  • Manifold learning-based data dimensionality-reduction method and device
  • Manifold learning-based data dimensionality-reduction method and device

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[0033] Below, in conjunction with accompanying drawing and specific embodiment, the present invention is described further:

[0034] Such as figure 1 As shown, the present invention provides a data dimensionality reduction method based on manifold learning, comprising the following steps:

[0035] S101: Divide the face image X to be detected into K sub-images according to certain rules, and then convert the K sub-images into corresponding sub-patterns, and the vector of the sub-patterns is denoted as: X i (i=1,2,...,K).

[0036] The method of dividing according to certain rules is to divide the face image into stripes, which can greatly preserve the texture structure of each part of the human face, thereby retaining more key feature information; The lower the data, the calculation is much lower than other manifold data dimensionality reduction methods.

[0037] S102: according to formula Y i =W i T x i Find Y i ; the Y i is x iA low-dimensional vector representation,...

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Abstract

The invention discloses a manifold learning-based data dimensionality-reduction method and a device. The method comprises the steps of firstly, dividing a to-be-detected human face image into sub-images according to an equal-strip rule and converting the image in a corresponding sub-mode; secondly, subjecting the divided to-be-detected human face image to the data dimensionality-reduction treatment; thirdly, classifying the low-dimensional vectors of the dimensionality-reduced image according to K sub-modes in a training set to obtain K recognition results; fourthly, calculating the K recognition results according to the weighting method to obtain a final recognition result of the to-be-detected human face image. Namely, the to-be-detected human face image can be recognized as one human face image in the training set.

Description

technical field [0001] The invention relates to a data dimensionality reduction method and device, in particular to a manifold learning-based data dimensionality reduction method and device. Background technique [0002] In recent years, with the rapid development of science and technology, the data obtained by people through various channels has greatly increased compared with the past. Therefore, using data dimensionality reduction technology to process these high-dimensional data has become an indispensable part of data processing. An important part of. Traditional dimensionality reduction methods (such as principal component analysis, independent component analysis, linear discriminant analysis, etc.) can effectively deal with data sets with linear structure. However, when the dataset has a nonlinear structure, it is difficult for these methods to discover the inherent low-dimensional information hidden in the high-dimensional data. Manifold learning-based data dimensi...

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Application Information

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IPC IPC(8): G06K9/00G06T3/00
CPCG06T3/0031G06V40/168G06V40/172
Inventor 廖晨钢钱广麟严君张吉孙刚
Owner GUANGZHOU HONGSEN TECH
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