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, Volume 78, Issue 21, pp 30397–30418 | Cite as

Two-dimensional locality adaptive discriminant analysis

  • Qin LiEmail author
  • Jane You
Article
  • 42 Downloads

Abstract

Two-dimensional Linear Discriminant Analysis (2DLDA), which is supervised and extracts the most discriminating features, has been widely used in face image representation and recognition. However, 2DLDA is inapplicable to many real-world situations because it assumes that the input data obeys the Gaussian distribution and emphasizes the global relationship of data merely. To handle this problem, we present a Two-dimensional Locality Adaptive Discriminant Analysis (2DLADA). Compared to 2DLDA, our method has two salient advantages: (1) it does not depend on any assumptions on the data distribution and is more suitable in real world applications; (2) it adaptively exploits the intrinsic local structure of data manifold. Performance on artificial dataset and real-world datasets demonstrate the superiority of our proposed method.

Keywords

2DLDA Dimensionality reduction Local geometric structure 

Notes

Acknowledgments

This work is supported by the Collaborative Innovation Platform of shenzhen institute of information technology, theShenzhen Fundamental Research fund under Grant JCYJ20160530141902978, the National Natural Science Foundation of China under Grant 61773302, and the Natural Science Foundation of Ningbo: 2018A610049. We thank Yinfeng Wang and Xiaojun Yang for their language editing, thank Deyan Xie for her writing assistance.

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Authors and Affiliations

  1. 1.School of Software EngineeringShenzhen Institute of Information TechnologyShenzhenChina
  2. 2.Department of ComputingHong Kong Polytechnic UniversityHong KongChina

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