Abstract:

Sparse Representation（SR） and Dictionary Learning（DL） have been widely used to encode the feature data and facilitate pattern classification. Existing methods generally use [l1/l2] norm or class-specific dictionary to enforce the class discriminative ability of the SR. The resulted class discriminative ability is limited. In this work, the training set is used as the synthesis dictionary for SR of the training samples because it provides the most natural class-specific dictionary. The class information of the training set can be used to enhance an ideal discriminative property of the SR：exact block diagonal structure, meaning that each data can be represented only by data-in-class. To make the test stage easy, an analysis dictionary and a linear classifier are learnt under the supervision of the discriminative SR of the training set. Once the analysis dictionary and the classifier are learnt, the test stage is very simple and computation efficient. The method is called Discriminative Analysis Dictionary and Classifier Learning （DADCL）. Extensive experiments show that the method has better classification performance.

Key words: discriminative analysis dictionary, classifier learning, sparse representation

摘要：

稀疏表示（Sparse Representation，SR）和字典学习（Dictionary Learning，DL）已被广泛用于编码特征数据并有助于模式分类。现有方法通常使用[l1/l2]范数或每类使用特定字典来强制SR的类判别能力，但由此产生的类判别能力有限。在这项工作中，提出使用训练集作为训练样本的SR的综合字典，因为它为每类数据提供了最自然的特定字典。训练集的类信息可用于增强SR的判别能力：精确块对角线结构，意味着每个数据只能由同类中数据表示。为了使测试阶段容易，在训练集的判别SR的监督下学习解析字典和线性分类器。一旦学习了解析字典和分类器，测试阶段就非常简单并且高效。称之为判别分析字典与分类器学习（Discriminative Analysis Dictionary and Classifier Learning，DADCL）。大量实验表明，该方法具有较好的分类性能。

关键词: 判别性解析字典, 分类器学习, 稀疏表示