Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, Jiangsu 210096, PR China.
Neural Netw. 2013 Oct;46:190-8. doi: 10.1016/j.neunet.2013.06.002. Epub 2013 Jun 10.
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.
鲁棒降维是处理多元数据的一个重要问题。基于 L1 范数的二维主成分分析（2DPCA-L1）是最近在图像域中提出的一种用于鲁棒降维的技术。然而，2DPCA-L1 的基向量仍然是密集的。对图像分析进行稀疏建模是有益的。在本文中，我们提出了一种新的降维方法，称为具有稀疏性的 2DPCA-L1（2DPCAL1-S），它有效地结合了 2DPCA-L1 的鲁棒性和稀疏诱导 lasso 正则化。它是用于无监督学习的 2DPCA-L1 的稀疏变体。我们精心设计了一种迭代算法来计算 2DPCAL1-S 的基向量。在图像数据集上的实验验证了所提出方法的有效性。
