Unsupervised cluster characteristic selection method based on Laplace regularization

Abstract

The invention discloses an unsupervised cluster characteristic selection method based on Laplace regularization. The unsupervised cluster characteristic selection method comprises the following steps: (1) constructing a sample characteristic matrix, (2) calculating a Laplace matrix, and (3) extracting the characteristics of the sample characteristic matrix. The unsupervised cluster characteristic selection method disclosed by the invention selects the characteristics through directly measuring the variance of follow-up study prediction results, and can directly enhance the follow-up study prediction results. Influence of the selected characteristics to predicted values of the study problems is taken into the consideration in the characteristic extraction process, so that the follow-up study efficiency can be efficiently improved. In addition, the modeling of data of the unsupervised cluster characteristic selection method disclosed by the invention is on the basis of a Laplace method of manifold geometry of the data. The unsupervised cluster characteristic selection method can efficiently reflect distribution information of the data in the space so as to calculate the maximum dimensionality of the information amount.

Description

technical field [0001] The invention belongs to the technical field of data processing, and in particular relates to an unsupervised clustering feature selection method based on Laplacian regularization. Background technique [0002] Clustering is a common multivariate statistical analysis method in machine learning and data mining. It discusses a large number of samples and requires reasonable classification according to their respective characteristics. There is no model for reference or to follow, that is, in performed without prior knowledge. At present, as an effective means of data analysis, clustering methods are widely used in various fields: in business, cluster analysis is used to discover different customer groups, and characterize the characteristics of different customer groups through purchase patterns; In biology, cluster analysis is used to classify animals and plants and to classify genes to gain an understanding of the inherent structure of populations; in...

AI Technical Summary

Problems solved by technology

[0009] These traditional clustering methods have successfully solved the clustering problem of low-dimensional data, but with the rapid development of information technology and the improvement of data collection capabilities, the dimension of data in various fields has increased exponentially. The complexity of traditional clustering methods often fail when dealing with many high-dimensional data

Because when traditional clustering methods cluster high-dimensional data sets, they mainly encounter two problems: (1) There are a large number of irrelevant attributes in high-dimensional data sets, making the possibility of clusters in all dimensions almost zero, which greatly increases (2) The disaster of dimensionality brought by high dimensions makes some clustering algorithms practically zero, which seriously affects the efficiency and effect of learning in many fields such as image, recognition, and information retrieval.

[0015] As a classic spectral method for feature selection, the Laplacian score has been widely used in various applications. This method can effectively find out the main features of the data, but it cannot effectively extract the category features of the data; Q-alpha As a feature selection method, it has a good effect on gene selection, but it is not suitable for other applications (such as image processing); variance feature selection method is one of the simplest feature selection methods, but it is only Select the feature with the largest change as the feature with the most information. This selection method is easily disturbed by noise data.

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