The aim of this study is to improve the classification efficiency of advanced methods using a multilayered dictionary learning framework. This paper presents the new idea of “multilayered K-singular value decomposition (MLK-SVD)” dictionary learning as a multilayer method of classification. This method starts by building a sparse representation at the patch level and relies on a hierarchy of learned dictionaries to output a global sparse representation for the whole image. In this research using class labels of training data, the label information is associated with each dictionary item (columns of the dictionary matrix) to enforce discrimination in sparse codes during the dictionary learning process. Also, this algorithm instead of learning one shallow dictionary learned multiple levels of dictionaries. The proposed formulation of deep dictionary learning provides the basis to develop more efficient dictionary learning algorithms. It relies on a succession of sparse coding and pooling steps in order to find an efficient representation of the data for classification. The performance of the proposed method is evaluated on MNIST and CIFAR-10 datasets, and results show that this method can help in advancing the state of the art.

中文翻译：

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MLK-SVD，深度字典学习的新方法

本研究的目的是使用多层字典学习框架提高先进方法的分类效率。本文提出了“多层K-奇异值分解（MLK-SVD）”字典学习作为一种多层分类方法的新思想。该方法首先在补丁级别构建稀疏表示，并依赖于学习字典的层次结构来输出整个图像的全局稀疏表示。在这项使用训练数据类标签的研究中，标签信息与每个字典项（字典矩阵的列）相关联，以在字典学习过程中强制区分稀疏代码。此外，该算法不是学习一个浅层字典，而是学习了多个级别的字典。提出的深度字典学习公式为开发更有效的字典学习算法提供了基础。它依赖于一系列稀疏编码和池化步骤，以便找到用于分类的数据的有效表示。所提出方法的性能在 MNIST 和 CIFAR-10 数据集上进行了评估，结果表明该方法有助于推进最先进的技术。