2DPCA with L1-norm for simultaneously robust and sparse modelling
2DPCA with L1-norm for simultaneously robust and sparse modelling
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DOI：
10.1016/j.neunet.2013.06.002
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发表时间：
2013-10
期刊：
影响因子：
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通讯作者：
Haixian Wang;Jing Wang
中科院分区：
文献类型：
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作者：
Haixian Wang;Jing Wang
稳健降维是多变量数据处理中的一个重要问题。基于L1范数的二维主成分分析(2DPCA-L1)是近年来发展起来的一种图像域稳健降维技术。然而，2DPCA-L1的基向量仍然是密集的。对图像分析执行稀疏建模是有益的。本文提出了一种新的降维方法，称为2DPCA-L1稀疏(2DPCAL1-S)，它有效地结合了2DPCA-L1的稳健性和稀疏诱导的套索正则化。它是2DPCA-L1的稀疏变体，用于非监督学习。我们精心设计了一个迭代算法来计算2DPCAL1-S的基向量。在图像数据集上的实验验证了该方法的有效性。
Robust dimensionality reduction is an important issue in processing multivariate data. Two-dimensional principal component analysis based on L1-norm (2DPCA-L1) is a recently developed technique for robust dimensionality reduction in the image domain. The basis vectors of 2DPCA-L1, however, are still dense. It is beneficial to perform a sparse modelling for the image analysis. In this paper, we propose a new dimensionality reduction method, referred to as 2DPCA-L1 with sparsity (2DPCAL1-S), which effectively combines the robustness of 2DPCA-L1 and the sparsity-inducing lasso regularization. It is a sparse variant of 2DPCA-L1 for unsupervised learning. We elaborately design an iterative algorithm to compute the basis vectors of 2DPCAL1-S. The experiments on image data sets confirm the effectiveness of the proposed approach.