Incoherent Dictionary Pair Learning: Application to a Novel Open-Source Database of Chinese Numbers
Incoherent Dictionary Pair Learning: Application to a Novel Open-Source Database of Chinese Numbers
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DOI：
10.1109/lsp.2018.2798406
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发表时间：
2018-01
影响因子：
3.9
通讯作者：
V. Abolghasemi;Mingyang Chen;Ali Alameer;S. Ferdowsi;Jonathon A. Chambers;K. Nazarpour
中科院分区：
文献类型：
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作者：
V. Abolghasemi;Mingyang Chen;Ali Alameer;S. Ferdowsi;Jonathon A. Chambers;K. Nazarpour
我们通过增加字典不一致惩罚项来提高现有字典对学习算法的有效性。在提出交替最小化解决方案后，我们将所提出的非相干字典对学习（InDPL）方法应用于一个新的开源中文数字数据库的分类。基准测试结果证实，InDPL算法提供了增强的分类精度，特别是当训练样本的数量是有限的。
We enhance the efficacy of an existing dictionary pair learning algorithm by adding a dictionary incoherence penalty term. After presenting an alternating minimization solution, we apply the proposed incoherent dictionary pair learning (InDPL) method in classification of a novel open-source database of Chinese numbers. Benchmarking results confirm that the InDPL algorithm offers enhanced classification accuracy, especially when the number of training samples is limited.