Multi-manifold based handwritten data classification method and system

Abstract

The invention discloses a multi-manifold based handwritten data classification method and system. The method comprises that a high-dimension data set is obtained; the similarity between any two data points in the high-dimension data set is calculated, and a similarity matrix corresponding to the high-dimension data set is obtained; according to the similarity matrix, a target Laplacian matrix corresponding to the high-dimension dataset is constructed; and a first preset number of former characteristic vectors in the target Laplacian matrix are gathered into a second preset number of classes, and a classification result of the high-dimension data set is obtained. The similarity matrix corresponding to the high-dimension data set is obtained, a bottom low-dimension mapping manifold structureof the high-dimension data is obtained, the Laplacian matrix taking both the high-dimension structure of high-dimension data and a bottom low-dimension mapping structure into consider can be constructed, the target Laplacian matrix is used to decompose characteristic values of the Laplacian matrix, the obtained characteristic values are clustered, a clustering result of the high-dimension data set is obtained, and an original structure of the high-dimension data is reserves as much as possible.

Description

technical field [0001] The invention relates to the field of pattern recognition, in particular to a method and system for classifying handwritten data based on multiple manifolds. Background technique [0002] Handwritten digit recognition has always been an important research topic in the field of pattern recognition, and has a very wide application prospect. With the rapid development of computer technology and digital image processing technology, digital recognition technology has been widely used in large-scale data statistics, mail sorting, finance, taxation and financial fields. However, handwritten digits are high-dimensional data. If it is directly recognized, it will not only take a long time, but also have a large computational complexity. [0003] In the prior art, handwritten digits are usually recognized after dimensionality reduction. At present, methods such as neural network feature extraction or dimensionality reduction and recognition are mostly used. Ho...

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Problems solved by technology

However, the above methods will destroy the original structure of the data, resulting in the loss of features.

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