Robust and sparse linear discriminant analysis via alternating direction method of multipliers
Robust and sparse linear discriminant analysis via alternating direction method of multipliers
复制标题
通过乘子交替方向法进行稳健和稀疏线性判别分析
DOI：
10.1109/tnnls.2019.2910991
复制
发表时间：
2020
影响因子：
10.4
通讯作者：
Liu M Z
中科院分区：
文献类型：
--
作者：
Li C N;Shao Y H;Yin W;Liu M Z
在本文中，我们提出了一个鲁棒的线性鉴别分析（RLDA）通过Bhattacharyya误差界优化。RLDA考虑了一个非凸问题，L1范数操作，使其不敏感的离群值和噪声比L2范数线性判别分析（LDA）。此外，我们将我们的RLDA扩展到稀疏模型（RSLDA）。RLDA和RSLDA都可以提取无限数量的特征，避免了小样本（SSS）的问题，和交替方向的乘法器（ADMM）的方法来科普所提出的公式中的非凸性。与传统的LDA相比，我们的RLDA和RSLDA对离群点和噪声具有更强的鲁棒性，RSLDA可以获得稀疏的鉴别方向。这些发现得到了人工数据集和人脸数据库实验的支持。
In this paper, we propose a robust linear discriminant analysis (RLDA) through Bhattacharyya error bound optimization. RLDA considers a nonconvex problem with the L1-norm operation that makes it less sensitive to outliers and noise than the L2-norm linear discriminant analysis (LDA). In addition, we extend our RLDA to a sparse model (RSLDA). Both RLDA and RSLDA can extract unbounded numbers of features and avoid the small sample size (SSS) problem, and an alternating direction method of multipliers (ADMM) is used to cope with the nonconvexity in the proposed formulations. Compared with the traditional LDA, our RLDA and RSLDA are more robust to outliers and noise, and RSLDA can obtain sparse discriminant directions. These findings are supported by experiments on artificial data sets as well as human face databases.