Deep Dictionary Learning
Deep Dictionary Learning
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DOI：
10.1109/access.2016.2611583
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发表时间：
2016-01-01
期刊：
影响因子：
3.9
通讯作者：
Vatsa, Mayank
中科院分区：
文献类型：
--
作者：
Tariyal, Snigdha;Majumdar, Angshul;Vatsa, Mayank
两种流行的表示学习范式是字典学习和深度学习。字典学习侧重于通过矩阵分解来学习“基础”和“特征”，而深度学习则侧重于通过以贪婪的方式逐层学习“权重”或“过滤器”来提取特征。本文重点通过提出深度字典学习来结合这两种范式的概念，并展示如何使用字典学习层构建更深层次的架构。所提出的技术与其他深度学习方法（例如堆叠自动编码器、深度置信网络和卷积神经网络）进行了比较。数据集表明，所提出的技术在电器分类的现实问题上实现了更高的分类和聚类精度，我们表明深度字典学习在其他技术无法产生同等性能的情况下表现出色，我们假设所提出的公式可以为新型深度学习工具铺平道路。
Two popular representation learning paradigms are dictionary learning and deep learning. While dictionary learning focuses on learning "basis'' and "features'' by matrix factorization, deep learning focuses on extracting features via learning "weights'' or filter'' in a greedy layer by layer fashion. This paper focuses on combining the concepts of these two paradigms by proposing deep dictionary learning and show how deeper architectures can be built using the layers of dictionary learning. The proposed technique is compared with other deep learning approaches, such as stacked autoencoder, deep belief network, and convolutional neural network. Experiments on benchmark data sets show that the proposed technique achieves higher classification and clustering accuracies. On a real-world problem of electrical appliance classification, we show that deep dictionary learning excels where others do not yield at-par performance. We postulate that the proposed formulation can pave the path for a new class of deep learning tools.