Data-dependent kernel sparsity preserving projection and its application for semi-supervised classification

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Abstract

Dimensionality reduction methods (DR) have been commonly used as a principled way to understand the high-dimensional data. In this paper, a novel semi-supervised nonlinear method called semi-supervised data-dependent kernel sparsity preserving projection (SDKSPP) is proposed for dimensionality reduction. To achieve performance improvements, SDKSPP adopts a data-dependent kernel (DK) instead of a standard kernel. The coefficients in DK are optimized with labeled samples by using the Fisher criterion. Then the labeled and unlabeled samples are mapped into a high dimensional space by DK. The sparse reconstructive relationship among the whole samples is calculated by minimizing a l1 regularization-related objective function. Finally, a transform matrix that can preserve this relationship is obtained to project the mapped data into a low-dimensional space. The effectiveness of the proposed method is tested and compared with seven methods on four popular datasets.

Keywords

Sparsity preserving projection Data-dependent kernel Semi-supervised learning Dimensionality reduction 

Notes

Acknowledgements

This work is partially supported by the National Natural Science Foundation of China (No. 61573088, No. 61573087 and No. 61433004).

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Information Science and EngineeringNortheastern UniversityShenyangChina

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