L1-norm and maximum margin criterion based discriminant locality preserving projections via trace Lasso
L1-norm and maximum margin criterion based discriminant locality preserving projections via trace Lasso
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DOI：
10.1016/j.patcog.2016.01.029
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发表时间：
2016-07
期刊：
影响因子：
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通讯作者：
Gui-Fu Lu;Jian Zou;Yong Wang
中科院分区：
文献类型：
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作者：
Gui-Fu Lu;Jian Zou;Yong Wang
基于最大间隔准则的判别式局部保持投影（DLPP/MMC）是一种有用的特征提取方法，在模式识别中表现出良好的性能。然而，传统的DLPP/MMC，是不健全的噪声和离群值，因为它的目标函数是基于L2-范数。本文提出了一种新的基于L1范数和最大间隔准则的判别式局部保持轨迹Lasso投影算法（DLPP/MMC-L1 TL）。在DLPP/MMC-L1 TL算法中，采用L1范数而不是L2范数，使其对噪声和离群点具有较强的鲁棒性。此外，为了进一步提高DLPP/MMC-L1 TL的性能，我们使用trace Lasso对基向量进行正则化。TraceLasso是最近提出的一种范数，它能够平衡L1范数和L2范数，同时考虑数据的稀疏性和相关性。本文还提出了一种求解DLPP/MMC-L1 TL的迭代方法。在多个数据集上的实验结果验证了DLPP/MMC-L1 TL的有效性。
Discriminant locality preserving projections based on maximum margin criterion (DLPP/MMC) is a useful feature extraction method since it has shown good performances in pattern recognition. The conventional DLPP/MMC, however, is not robust to noises and outliers since its objective function is based on L2-norm. In this paper, we propose a novel L1-norm and maximum margin criterion based discriminant locality preserving projections via trace Lasso (DLPP/MMC-L1TL). L1-norm rather than L2-norm is used in the formulation of DLPP/MMC-L1TL, which makes it be robust to noises and outliers. Besides, in order to improve the performance of DLPP/MMC-L1TL further, we use trace Lasso to regularize the basis vectors. Trace Lasso, which can balance L1-norm and L2-norm and consider sparsity and correlation of data simultaneously, is a recently proposed norm. An iterative procedure for solving DLPP/MMC-L1TL is also proposed in this paper. The experiment results on some data sets demonstrate the effectiveness of DLPP/MMC-L1TL.