Transductive Data Dimensionality Reduction Method Based on Supervised Graph
A data dimensionality reduction and supervision graph technology, applied in the field of image processing, can solve the problems of not considering the sample class label information, unsatisfactory classification and recognition effect, etc., and achieve the effect of improving data classification effect and recognition performance
Active Publication Date: 2019-03-26
XIDIAN UNIV
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However, this method does not consider the class label information of the sample when projecting the k-nearest neighbor map
[0007] Therefore, the above-mentioned SELF and TCA methods are not ideal for classification and recognition of data after dimensionality reduction.
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[0038] The specific implementation steps and effects of the present invention will be further described in detail below in conjunction with the accompanying drawings.
[0039] refer to figure 1 , the implementation steps of the present invention are as follows:
[0040] Step 1. Input the original image.
[0041] Input n=F×P original images, and after calibrating and aligning these images, they are cropped to the same size, where F is the number of original image categories, and P is the number of images of each category.
[0042] Step 2. Use the original image to obtain the original matrix X.
[0043] The gray feature value of each original image pixel is taken out by row, and arranged in sequence to form a d-dimensional row vector, forming an n×d matrix X', and normalizing each row of the matrix X', so that the matrix The sum of the elements of each row of X' is equal to 1: Among them, v' j is the jth row vector of matrix X', x' i is the row vector v' j i-th element, ...
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The invention discloses a transductive local-preserving projection data dimensionality reduction method, which mainly solves the problem that the existing semi-supervised learning-based data dimensionality reduction method only uses Euclidean distance for composition, and the recognition result is unsatisfactory. The implementation steps are: (1) input data and normalize; (2) calculate the normalized original matrix and class label vector; (3) calculate the first Laplacian matrix L from the original data; (4) Calculate the second Laplacian matrix L from the class scalar vector l ; (5) by the first Laplacian matrix L and the second Laplacian matrix L l Calculate the similarity matrix S; (6) Calculate the inter-class weight matrix W of the sample from the class label vector c ; (7) From the similarity matrix S and the inter-class weight matrix W c Construct the generalized eigenvalue formula and solve it to obtain the projection matrix E; (8) Calculate the dimension-reduced samples from the projection matrix E. The invention can effectively perform feature extraction and dimension reduction on data, improve the accuracy of data classification and recognition, and can be used for data and image processing.
Description
technical field [0001] The invention belongs to the technical field of image processing, in particular to a dimensionality reduction method for high-dimensional data, which can be used for data and computer image recognition. Background technique [0002] In recent years, with the development of computer technology and manufacturing industry, smart devices have become popular, such as smart phones, smart bracelets and so on. Almost every smart device has a large number of sensors to collect various data. The popularity of a large number of smart devices has been accompanied by an explosive growth of raw data. When we get more and more data, how to make full use of the information in the data has become the focus of academic research. Data dimensionality reduction is an effective means to solve these problems. Data dimensionality reduction aims to use popular computers to automatically explore the information in the original data and discover the essential features hidden ...
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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/172G06F18/24143
Inventor 王磊姬红兵王家俊朱明哲李丹萍
Owner XIDIAN UNIV