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Sparse coding method based on local and global regularization

A sparse coding and global technology, applied in the field of sparse coding, which can solve the problem that low-dimensional representation cannot maintain the local and global geometric structure of data at the same time.

Inactive Publication Date: 2018-04-10
JIANGSU UNIV OF TECH +1
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Problems solved by technology

[0021] In order to solve the defect that the low-dimensional representation in the prior art cannot maintain the local and global geometric structure of the data at the same time, the present invention provides a sparse coding method based on local and global regularization. Compared with the traditional sparse coding method, the method constructs Local regression regularization to maintain the local structure information of the data, and kernel regression to capture the global geometric structure of the data

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  • Sparse coding method based on local and global regularization
  • Sparse coding method based on local and global regularization
  • Sparse coding method based on local and global regularization

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Embodiment Construction

[0092] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0093] This embodiment proposes a sparse coding method based on local and global regularization. The objective function and solution method of this method are as follows:

[0094] (1) Local and global regularization

[0095] Traditional sparse coding methods cannot capture the inherent geometric structure in the data because they only exploit the local or global structure of the data alone. A reasonable approach is to make full use of the local and global structural information of the data during the representation process.

[0096] A large number of studies have shown that in the data representation, making full use of the local geometric structure of the data can improve the performance of the algorithm. Therefore, the present invention exploits local regression regularization to discover the underlying geometric structure of the data. Specifically...

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Abstract

The invention discloses a sparse coding method based on local and global regularization, pertaining to the technical field of sparse coding (SC). The method utilizes local regression regularization tofind out a potential geometric structure of data. Concretely speaking, the overall data space is divided into a large number of partial regions. Each sample is linearly represented by a sample of a partial region to which each sample belongs, which is called as a local learning conception. On the basis of the local learning conception, a kernel global regression method is utilized for grabbing aglobal geometric structure of data. Therefore, partial and global regularization are utilized for obtaining an inherent geometric structure of data.

Description

technical field [0001] The invention belongs to the technical field of sparse coding (SC), in particular to a sparse coding method based on local and global regularization. Background technique [0002] In many classification and clustering problems, the processing of high-dimensional data is a very challenging problem. In order to solve this problem, people often use data representation methods to find low-dimensional representations in high-dimensional data, so as to achieve the purpose of improving computing efficiency and reducing storage space. At present, data representation methods have attracted the attention of many scholars due to their excellent representation in computer vision, information retrieval and machine learning. [0003] Principal component analysis (PCA) and linear discriminant analysis (LDA) are two popular linear representation methods, the former is an unsupervised learning method whose purpose is to find the minimum covariance projection direction...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/15
CPCG06F17/15
Inventor 舒振球朱琪范洪辉张杰
Owner JIANGSU UNIV OF TECH
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