Simultaneous discriminative projection and dictionary learning for sparse representation based classification
Simultaneous discriminative projection and dictionary learning for sparse representation based classification
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DOI：
10.1016/j.patcog.2012.07.010
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Haichao Zhang-;Yanning Zhang;Thomas S. Huang
中科院分区：
文献类型：
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作者：
Haichao Zhang-;Yanning Zhang;Thomas S. Huang
稀疏驱动分类方法由于其在各种分类任务中的有效性，近年来得到了广泛的应用。它是基于同一类样本在同一子空间的假设，因此一个测试样本可以很好地用同一类的训练样本来表示。以前的方法要么直接使用训练样本，要么分别使用为每个类训练的字典对每个类的子空间建模。这些方法虽然具有较强的重构能力，但可能不具有理想的判别能力，特别是当不同类别的样本之间存在较高的相关性时。在本文中，我们提出同时学习针对稀疏表示的分类器优化的判别投影和字典，在尊重稀疏表示假设的情况下从原始数据中提取判别信息。通过将投影和字典学习任务形成一个优化框架，我们可以有效地学习判别投影和字典。在不同的数据集上进行了大量的实验，实验结果验证了该方法的有效性。
Sparsity driven classification method has been popular recently due to its effectiveness in various classification tasks. It is based on the assumption that samples of the same class live in the same subspace, thus a test sample can be well represented by the training samples of the same class. Previous methods model the subspace for each class with either the training samples directly or dictionaries trained for each class separately. Although enabling strong reconstructive ability, these methods may not have desirable discriminative ability, especially when there are high correlations among the samples of different classes. In this paper, we propose to learn simultaneously a discriminative projection and a dictionary that are optimized for the sparse representation based classifier, to extract discriminative information from the raw data while respecting the sparse representation assumption. By formulating the task of projection and dictionary learning into an optimization framework, we can learn the discriminative projection and dictionary effectively. Extensive experiments are carried out on various datasets and the experimental results verify the efficacy of the proposed method.