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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Learning Stable Multilevel Dictionaries for Sparse Representations
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Learning Stable Multilevel Dictionaries for Sparse Representations

机译：为稀疏表示学习稳定的多级词典

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摘要

Sparse representations using learned dictionaries are being increasingly used with success in several data processing and machine learning applications. The increasing need for learning sparse models in large-scale applications motivates the development of efficient, robust, and provably good dictionary learning algorithms. Algorithmic stability and generalizability are desirable characteristics for dictionary learning algorithms that aim to build global dictionaries, which can efficiently model any test data similar to the training samples. In this paper, we propose an algorithm to learn dictionaries for sparse representations from large scale data, and prove that the proposed learning algorithm is stable and generalizable asymptotically. The algorithm employs a 1-D subspace clustering procedure, the -hyperline clustering, to learn a hierarchical dictionary with multiple levels. We also propose an information-theoretic scheme to estimate the number of atoms needed in each level of learning and develop an ensemble approach to learn robust dictionaries. Using the proposed dictionaries, the sparse code for novel test data can be computed using a low-complexity pursuit procedure. We demonstrate the stability and generalization characteristics of the proposed algorithm using simulations. We also evaluate the utility of the multilevel dictionaries in compressed recovery and subspace learning applications.
机译：在许多数据处理和机器学习应用程序中，越来越成功地使用使用学习词典的稀疏表示。在大规模应用中对学习稀疏模型的需求不断增长，促使人们开发高效，健壮且可证明是好的字典学习算法。算法稳定性和通用性是旨在构建全局词典的字典学习算法的理想特性，该字典学习算法可以有效地对类似于训练样本的任何测试数据进行建模。本文提出了一种从大规模数据中学习稀疏表示的字典的算法，并证明了该学习算法的稳定性和渐近性。该算法采用一维子空间聚类过程--hyperline聚类，以学习具有多个级别的分层字典。我们还提出了一种信息理论方案，以估计每个学习水平所需的原子数，并开发一种集成方法来学习鲁棒的词典。使用建议的字典，可以使用低复杂度追踪程序来计算新测试数据的稀疏代码。我们通过仿真证明了所提出算法的稳定性和泛化特性。我们还评估了多级字典在压缩恢复和子空间学习应用程序中的效用。

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