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Multi-view classification method based on indefinite kernels

A classification method and multi-view technology, applied to instruments, character and pattern recognition, computer components, etc., can solve problems that are difficult to solve effectively, labels are incomplete, and cannot be solved

Inactive Publication Date: 2015-07-29
SOUTHEAST UNIV
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Problems solved by technology

Therefore, incomplete labeling on multiple views is one of the problems in multi-view learning, and none of the existing multi-view learning methods can effectively solve this problem

Method used

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  • Multi-view classification method based on indefinite kernels
  • Multi-view classification method based on indefinite kernels
  • Multi-view classification method based on indefinite kernels

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Embodiment Construction

[0050] The present invention will be described in detail below in conjunction with accompanying drawing and example, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalents of the present invention Modifications of form are subject to the appended rights of this application.

[0051] The present invention mainly aims at the problem of incomplete labels in multi-view learning, and combines with an indeterminate kernel method in order to obtain better classification performance. The problems faced mainly include the following three aspects:

[0052] 1. In multi-view learning, it is very difficult to obtain a large number of labeled samples. In the case of a small number of labeled samples, it is even more difficult to ensure that the data labeling of each view is complete. H...

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Abstract

The invention discloses a multi-view classification method based on indefinite kernels. The method includes the following steps of firstly, obtaining a multi-view image set used for conducting training; secondly, generating a projection matrix through multi-view data, and projecting different view data in a united low-dimension space; thirdly, training samples in the low-dimension projection space through the definite kernel technology to obtain a classifier; fourthly, standardizing new multi-view data sets, projecting the data sets into the trained low-dimension space, and inputting the projected data sets into the classifier obtained through training so as to obtain the classification result. By means of the method, the mark-number-incomplete multi-view classification problem which needs to be solved is converted into the single-view semi-supervised classification problem in the united low-dimension space, and the mark number integrity on a single view can be achieved; judgment information of data with mark numbers and structure information of data without mark numbers are fully utilized, and the performance of the classifier is improved; in addition, new multi-view data can be directly tested and classified.

Description

technical field [0001] The invention relates to the technical field of pattern recognition and machine learning, in particular to a multi-view classification method based on an indeterminate kernel. Background technique [0002] The kernel method is one of the core technologies in machine learning, and it is an important method to solve the nonlinear learning problems in practical problems. Its core idea is to embed the original data into the high-dimensional feature space through a nonlinear mapping, and then Analyze and process data in new spaces using linear learners. Its advantages are mainly reflected in that it does not need to know the specific form and parameters of the nonlinear mapping in advance, but introduces the kernel function, and implicitly realizes the mapping from the low-dimensional input space to the high-dimensional feature space by changing the form and parameters of the kernel function; The kernel function can transform the complex inner product oper...

Claims

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Application Information

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IPC IPC(8): G06K9/62
Inventor 薛晖
Owner SOUTHEAST UNIV
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