Learning Mixtures of Separable Dictionaries for Tensor Data: Analysis and Algorithms
Learning Mixtures of Separable Dictionaries for Tensor Data: Analysis and Algorithms
复制标题
DOI：
10.1109/tsp.2019.2952046
复制
发表时间：
2019-03
影响因子：
5.4
通讯作者：
Mohsen Ghassemi;Z. Shakeri;A. Sarwate;W. Bajwa
中科院分区：
文献类型：
--
作者：
Mohsen Ghassemi;Z. Shakeri;A. Sarwate;W. Bajwa
这项工作解决了使用结构化字典学习来学习张量数据的稀疏表示的问题。它建议通过推广可分离字典学习模型来学习可分离字典的混合，以更好地捕获张量数据的结构。探索了两种不同的学习可分离字典混合的方法，并在每种情况下导出了底层字典局部可识别性的充分条件。此外，还开发了计算算法来解决批量和在线设置中可分离词典的学习混合问题。数值实验用于证明所提出模型的有用性和所开发算法的有效性。
This work addresses the problem of learning sparse representations of tensor data using structured dictionary learning. It proposes learning a mixture of separable dictionaries to better capture the structure of tensor data by generalizing the separable dictionary learning model. Two different approaches for learning mixture of separable dictionaries are explored and sufficient conditions for local identifiability of the underlying dictionary are derived in each case. Moreover, computational algorithms are developed to solve the problem of learning mixture of separable dictionaries in both batch and online settings. Numerical experiments are used to show the usefulness of the proposed model and the efficacy of the developed algorithms.