Abstract Dictionary learning methods can be split into: i) class specific dictionary learning ii) class shared dictionary learning. The difference between the two categories is how to use discriminative information. With the first category, samples of different classes are mapped into different subspaces, which leads to some redundancy with the class specific base vectors. While for the second category, the samples in each specific class can not be described accurately. In this paper, we first propose a novel class shared dictionary learning method named label embedded dictionary learning (LEDL). It is the improvement based on LCKSVD, which is easier to find out the optimal solution. Then we propose a novel framework named cascaded dictionary learning framework (CDLF) to combine the specific dictionary learning with shared dictionary learning to describe the feature to boost the performance of classification sufficiently. Extensive experimental results on six benchmark datasets illustrate that our methods are capable of achieving superior performance compared to several state-of-art classification algorithms.

中文翻译：

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特定于类还是共享类？用于图像分类的级联字典学习框架

摘要 字典学习方法可以分为：i) 类特定字典学习 ii) 类共享字典学习。这两个类别的区别在于如何使用判别信息。对于第一类，不同类的样本被映射到不同的子空间，这导致特定于类的基向量存在一些冗余。而对于第二类，无法准确描述每个特定类中的样本。在本文中，我们首先提出了一种名为标签嵌入字典学习（LEDL）的新型类共享字典学习方法。是基于LCKSVD的改进，更容易找到最优解。然后我们提出了一个名为级联字典学习框架（CDLF）的新框架，将特定字典学习与共享字典学习相结合来描述特征，以充分提高分类性能。在六个基准数据集上的大量实验结果表明，与几种最先进的分类算法相比，我们的方法能够实现卓越的性能。