Soft Computing

, Volume 21, Issue 18, pp 5425–5441 | Cite as

Cognitive gravitation model-based relative transformation for classification

Methodologies and Application

Abstract

The classifiers based on the relative transformation have good effectiveness in classification on the noisy, sparse and high-dimensional data. However, the relative transformation only simply transforms features from the original space to the relative space by Euclidean distances. It still ignores many other human perceptions. For example, to identify an object, human may find the difference among similar objects and adjust the recognition results according to the densities of classes. To simulate these two human perceptions, this paper first modifies the cognitive gravity model with a new way to estimate mass and then Gaussian transformation, and then applies the modified model to redefine the relative transformation, denoted as CGRT. Subsequently, a new classifier is designed, which utilizes CGRT to transform the original space to the relative space in which the classification is performed. The conducted experiments on challenging benchmark datasets validate the CGRT and the designed classifier.

Keywords

Cognitive laws Classification Feature transformation Face recognition 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Jiaxing UniversityJiaxingChina
  2. 2.South China University of TechnologyGuangzhouChina

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