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Domain Transfer Extreme Learning Machine Method Based on Manifold Regularization and Norm Regularization

A technology of extreme learning machine and domain, applied in the field of transfer extreme learning machine algorithm, which can solve the problems of data offset and lack of domain transfer ability.

Active Publication Date: 2020-04-07
OCEAN UNIV OF CHINA
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, traditional ELM requires a large amount of labeled data to train the classifier, and requires the training data and the target to be recognized to have the same distribution characteristics, that is, it does not have domain transfer capabilities, etc.
In real life, labeling the data will consume a lot of manpower and material resources, and the target to be identified and the training data may not necessarily meet the condition of the same distribution. Seawater turbidity, geological characteristics, etc.) and other factors, the images collected by AUV in two different sea areas on the seabed will have data offset phenomenon and the training sample data with prior knowledge (labeled data) is often a small amount

Method used

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  • Domain Transfer Extreme Learning Machine Method Based on Manifold Regularization and Norm Regularization
  • Domain Transfer Extreme Learning Machine Method Based on Manifold Regularization and Norm Regularization
  • Domain Transfer Extreme Learning Machine Method Based on Manifold Regularization and Norm Regularization

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Embodiment Construction

[0046] In order to better understand the present invention, the present invention will be further described in detail below in conjunction with specific examples, but the following description is only for demonstration and explanation, and does not limit the present invention in any form.

[0047] The data used in this embodiment comes from the UCI machine learning database. The database contains data of 13,910 gas samples of 6 gases collected by an electronic nose system on a gas transmission platform for 36 consecutive months. In this embodiment, each sample is characterized by extracting a 128-dimensional feature. Since the gas detection sensor of the electronic nose system will have sensor drift with time, the collected gas data will also have data drift in different time periods. The flow process of the inventive method is as figure 1 shown.

[0048] Step 1: Take the data of 445 gas samples in the first and second months as the source field data of the sensorless drift...

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Abstract

The invention discloses a domain transfer extreme learning machine method based manifold regularization and norm regularization. On the basis of a traditional extreme learning machine, the thought of semi-supervised learning and transfer learning is introduced, and a novel extreme learning machine model is built and consists of three parts: a manifold regularization term capable of excavating geometric distribution shapes of data samples with tags and without tags to realize semi-supervised learning; a loss function term considering error minimization of source domain data and target domain data to realize transfer learning; and norm regularizers constraining weight space. The domain transfer extreme learning machine method provide by the invention is combined with the source domain to process the problem of prediction of the target domain, thereby increasing the generalization capability and range of application of the extreme learning machine. Introduction of the manifold regularization term also enables the method proposed by the invention to still maintain a relatively good learning effect when data with tags are little, the restriction that a traditional machine learning method requires a large amount of data with tags is overcome, and the accuracy and robustness of prediction are also improved.

Description

technical field [0001] The invention relates to a field transfer extreme learning machine algorithm based on manifold regular items and Lp norm regularizers, and belongs to the technical fields of machine learning and pattern recognition. Background technique [0002] Artificial neural network has been widely used in various fields such as biology, chemistry, medicine, economy and ocean because of its powerful self-adaptation, self-organization, self-learning and nonlinear mapping capabilities. However, the traditional neural network, such as the Back Propagation (BP) network, needs to manually set a large number of network training parameters, the training speed is slow, and it is easy to generate a local optimal solution. In response to the above problems, Huang proposed a new algorithm for Single-hidden Layer Feedforward Neural Networks (SLFNs) called Extreme Learning Machine (ELM). The core of the algorithm is mainly two parts: one is to randomly generate input weights ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N20/00
CPCG06N20/00
Inventor 年睿蔡文强王耀民
Owner OCEAN UNIV OF CHINA
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