Local and global consistence classifying method based on sparse decomposition of figure 10

A technology of sparse decomposition and classification method, applied in the field of semi-supervised classification based on graph, which can solve the problems of large amount of calculation and inapplicability to obtain the most similar nodes of the graph, and achieve the effect of improving the generalization ability and classification accuracy.

Inactive Publication Date: 2012-06-20
NORTHWESTERN POLYTECHNICAL UNIV
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  • Abstract
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Problems solved by technology

However, this process is not suitable for obtaining the most similar nodes of the graph, and because the basis tracking

Method used

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  • Local and global consistence classifying method based on sparse decomposition of figure 10
  • Local and global consistence classifying method based on sparse decomposition of figure 10
  • Local and global consistence classifying method based on sparse decomposition of figure 10

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Experimental program
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Embodiment Construction

[0036] Now in conjunction with embodiment, accompanying drawing, the present invention will be further described:

[0037] Step 1: Sparse decomposition l 0 graph construction

[0038] Step a: Use the KMEANS clustering algorithm to divide the entire data X to be classified into k subsets, where k is the number of categories;

[0039] Step b: Define an n×n adjacency matrix W, whose elements are initialized to 0, and n is the total number of data to be classified;

[0040] Step c: Take the i-th data x to be classified i For the signal to be decomposed, the sparse decomposition dictionary D i by x i The cluster subsets to which it belongs, x i not included in D i Among them, 1≤i≤n, n is the total number of data to be classified;

[0041] Step d: The preset maximum number of iterations is T=5, and the iteration termination error is ε stop =0.001, the current iteration number t is initialized to 0, and the decomposition signal x i , based on the dictionary D i use

[0042...

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Abstract

The invention relates to a local and global consistence classifying method based on sparse decomposition of a figure 10. Sparse decomposition is thoroughly used for effectively capturing the substantive characteristics of a signal, the characteristics of an interior structure and a greedy selection principle of matching pursuit; and an adjoining structure and a weight value of the figure 10 are determined according to a sparse decomposition coefficient and are applied to the local and global consistence classifying method. Indicated by an experimental result, a better classifying result is obtained in the local and global consistence classifying method compared with a traditional figure composition method and a 11 figure composition method.

Description

technical field [0001] The present invention relates to a method based on sparse decomposition l 0 The invention provides a local global consistency classification method for graphs, which belongs to a graph-based semi-supervised classification method. Background technique [0002] In recent years, semi-supervised learning using a small amount of labeled data and a large amount of unlabeled data has been deeply studied. This method can improve the performance of the classifier while reducing the cost of manual labeling. Its research results have been widely used in web page retrieval and image classification. and other fields. Existing semi-supervised learning methods include: Self-training algorithm, Co-training algorithm, semi-supervised SVM, and graph-based semi-supervised classification methods. [0003] Graph-based semi-supervised learning methods usually define a graph first. The nodes on the graph are a collection of labeled and unlabeled data. The weight of the edg...

Claims

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Application Information

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IPC IPC(8): G06K9/62
Inventor 李映张晓洁
Owner NORTHWESTERN POLYTECHNICAL UNIV
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