Manifold learning-based data dimensionality-reduction method and device

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...

AI Technical Summary

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 algorithm, this local change will inevitably have a great impact on the recognition results.

[0005] Secondly, when the LPP algorithm uses high-dimensional vectors to represent image data, the computational complexity will increase exponentially with the increase of the image dimension when encountering a singular matrix, which will inevitably consume a lot of computing resources and reduce the running speed of the algorithm. degrades overall system performance

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