Locality Preserving Projection Based on F-norm

Authors

  • Xiangjie Hu Beijing University of Technology
  • Yanfeng Sun Beijing University of Technology
  • Junbin Gao University of Sydney Business School, University of Sydney, Australia
  • Yongli Hu Beijing University of Technology
  • Baocai Yin Dalian University of Technology

DOI:

https://doi.org/10.1609/aaai.v32i1.11518

Abstract

Locality preserving projection (LPP) is a well-known method for dimensionality reduction in which the neighborhood graph structure of data is preserved. Traditional LPP employ squared F-norm for distance measurement. This may exaggerate more distance errors, and result in a model being sensitive to outliers. In order to deal with this issue, we propose two novel F-norm-based models, termed as F-LPP and F-2DLPP, which are developed for vector-based and matrix-based data, respectively. In F-LPP and F-2DLPP, the distance of data projected to a low dimensional space is measured by F-norm. Thus it is anticipated that both methods can reduce the influence of outliers. To solve the F-norm-based models, we propose an iterative optimization algorithm, and give the convergence analysis of algorithm. The experimental results on three public databases have demonstrated the effectiveness of our proposed methods.

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Published

2018-04-25

How to Cite

Hu, X., Sun, Y., Gao, J., Hu, Y., & Yin, B. (2018). Locality Preserving Projection Based on F-norm. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11518

Issue

Section

AAAI Technical Track: Heuristic Search and Optimization