Domain self-adaptive dimensionality reduction method through keeping maximum dependency relationship between data conversion

A technology of dependency and domain adaptation, applied in the fields of instruments, character and pattern recognition, computer parts, etc., can solve problems such as inapplicability, and achieve the effect of good applicability, intuitive physical meaning, and simple structure

Inactive Publication Date: 2018-03-20
SUN YAT SEN UNIV
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Problems solved by technology

The method of self-labeling target domain instances is also not suitable for high divergence domains

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  • Domain self-adaptive dimensionality reduction method through keeping maximum dependency relationship between data conversion
  • Domain self-adaptive dimensionality reduction method through keeping maximum dependency relationship between data conversion
  • Domain self-adaptive dimensionality reduction method through keeping maximum dependency relationship between data conversion

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Embodiment Construction

[0030] The present invention mainly provides a domain adaptive dimensionality reduction learning method. The technical solution of the present invention is to map the input samples of the source domain and the target domain to the regenerating kernel Hilbert space, and then re-project to another regenerating kernel Hilbert space through the transfer matrix, and finally realize dimensionality reduction. A method for adaptive dimensionality reduction in unsupervised and semi-supervised domains. The specific principle of the present invention is introduced as follows.

[0031] make represents the source domain samples, Represents the target domain sample, then the input sample is X={X S , X T}∈R D×N , H 1 Represents the regenerated kernel Hilbert space with k as the kernel function, let φ(x)=k(·,x), and φ:X→H 1 , H 1 =span{φ(x 1 ),…, φ(x N )}. definition The transfer matrix W is expressed as

[0032] In addition to the kernel matrix

[0033]

[0034] Therefo...

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Abstract

The invention relates to the domain self-adaptive correlation problem in the machine learning field and provides a domain self-adaptive dimensionality reduction method through keeping a maximum dependency relationshipship between data conversion. In order to reduce the distribution difference between a source domain and a target domain, the two-time regeneration nuclear hilbert space mapping is carried out on data, wherein two hilbert spaces are respectively marked as H1 and H2. The H2 is called as a common potential space between the source domain and the target domain. In order to conveniently measure the difference of edge distribution between the source region and the target region, the measurement is achieved by using a maximum mean value difference (MMD) method. The H2 is obtained through the mapping of the H1. In order to obtain the correlationship of the data mapping measurement between the space H1 and the space H2, the hilbert-schmidt independent criterion (HIC) method is used for measurement. The method mainly aims to minimize the distribution difference of the source domain and the target domain in the H2, namely the MMD value is minimized.The dependency relationshipship between data conversion in the space H1 and the space H is maximized. That is, the HIC value is maximized.

Description

technical field [0001] The invention relates to a domain adaptive technology oriented to the field of machine learning, in particular to a domain adaptive dimensionality reduction learning method. Background technique [0002] With the development of science and technology, the data information that people deal with is becoming more and more complex and huge. These data often have a high dimensionality, and these data generally have a lot of redundant information. Feature extraction is necessary. [0003] In the past machine learning research, there have been many studies on dimensionality reduction methods for feature extraction. The most typical linear dimensionality reduction methods include PCA and LDA (Document 1, Keinosuke Fukunaga. Introduction to statistical pattern recognition. Academic Press, 1972.). Typical popular learning nonlinear dimensionality reduction methods include ISOMAP (document 2, Tenenbaum JB, Silva VD, Langford JC (2000) A Global Geometric Framewo...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/21322G06F18/21324
Inventor 马争鸣欧阳效源刘洁刘希刘耀辉王鑫
Owner SUN YAT SEN UNIV
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