Earticle

초록

영어

Manifold learning is an approach for nonlinear dimensionality reduction and has been a hot research topic in the field of computer science. A disadvantage of manifold learning methods is, however, that there are no explicit mappings from the high-dimensional feature space to the low-dimensional representation space. It restricts the application of manifold learning methods in many practical problems such as target detection and classification. Previously, some methods have been proposed to provide linear or nonlinear mappings for manifold learning methods. However, a disadvantage of all these methods is that the learned projective functions are combinations of all the original features, thus it is often difficult to interpret the results. Moreover, the dense projection matrices of these approaches lead to a high cost of computation and storage. In this paper, a sparse polynomial mapping approach is proposed for manifold learning. We first get the low-dimensional representations of the high-dimensional input data by using a manifold learning method, and then a 𝑙1-based simplified polynomial regression is used to get a sparse polynomial mapping between the high-dimensional data and their low-dimensional representations. In particular, we apply this to the method of Laplacian eigenmap and derive a sparse nonlinear manifold learning algorithm, which is named sparse locality preserving polynomial embedding. Experimental results on real-world data show the effectiveness of our approach.

목차

Abstract
 1. Introduction
 2. Sparse Polynomial Mapping
 3. Sparse Locality Preserving Polynomial Embedding
 4. Experiments
  4.1. Datasets and Experimental Settings
  4.2. Experimental Results
 5. Conclusion
 Acknowledgments
 References

키워드

저자

• Ying Xia [ Research Center of Spatial Information System, Chongqing University of Posts and Telecommunications, Chongqing 400065, China ]
• Qiang Lu [ Research Center of Spatial Information System, Chongqing University of Posts and Telecommunications, Chongqing 400065, China ]
• Hae-Young Bae [ Research Center of Spatial Information System, Chongqing University of Posts and Telecommunications, Chongqing 400065, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605