Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8960114 | Neurocomputing | 2018 | 9 Pages |
Abstract
As a famous linear manifold learning method, orthogonal neighborhood preserving projections (ONPP) is able to provide a set of orthogonal projections for dimensionality reduction. However, a problem of ONPP is that it takes the L2-norm as the basic measurement and therefore tends to be sensitive to the outliers or the variations of the data. Aiming at strengthening the robustness of the conventional method ONPP, in this paper, a robust and sparse dimensionality reduction method based on linear reconstruction, called Robust Jointly Sparse Embedding (RJSE), is proposed by introducing L2, 1-norm as the basic measurement and regularization term. We design a simple iterative algorithm to obtain the optimal solution of the proposed robust and sparse dimensionality reduction model. Experiments on four benchmark data sets demonstrate the competitive performance of the proposed method compared with the state-of-the-art methods.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Zhihui Lai, Yudong Chen, Dongmei Mo, Jiajun Wen, Heng Kong,