Sparse L1-norm two dimensional linear discriminant analysis via the generalized elastic net regularization
Sparse L1-norm two dimensional linear discriminant analysis via the generalized elastic net regularization
复制标题
基于广义弹性网正则化的稀疏 L1 范数二维线性判别分析
DOI：
10.1016/j.neucom.2019.01.049
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发表时间：
2019-04-14
期刊：
影响因子：
6
通讯作者：
Wang, Zhen
中科院分区：
文献类型：
--
作者：
Li, Chun-Na;Shang, Meng-Qi;Wang, Zhen
线性判别分析（LDA）和二维LDA（2DLDA）是广泛应用的方法，用于降低维度。但是，他们俩都缺乏健壮和稀疏。最近的研究表明，弹性网和L1-norm将提高降低维度的学习能力。在本文中，我们提出了一个广义的弹性网，并将其应用于基于L1的LDA和2DLDA中，以鲁棒性和稀疏性同时扩展LDA和2DLDA（分别命名为LDAL1-S和2DLDAL1-S）。广义弹性网还有助于LDAL1-S和2DLDAL1-S避免奇异性问题。此外，LP-norm（0 <p
Linear discriminant analysis (LDA) and two dimensional LDA (2DLDA) are widely applied methods for dimensionality reduction. However, both of them lack of robustness and sparseness. Recent studies show that the elastic net and the L1-norm would improve the learning ability of dimensionality reduction. In this paper, we propose a generalized elastic net and apply it into the L1-norm based LDA and 2DLDA to extend LDA and 2DLDA with robustness and sparseness simultaneously (named LDAL1-S and 2DLDAL1-S, respectively). The generalized elastic net also helps LDAL1-S and 2DLDAL1-S avoiding the singularity problem. Moreover, the Lp-norm (0 < p