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| 基于动态赋权近邻传播的数据增量采样方法 |
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陈晓琪1,2, 谢振平1,2, 刘渊1,2, 詹千熠1,2
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1.江南大学 人工智能与计算机学院, 江苏 无锡 214122;2.江苏省媒体设计与软件技术重点实验室(江南大学), 江苏 无锡 214122
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| 摘要: |
| 数据采样是快速提取大规模数据集中有用信息的重要手段，为更好地应对越来越大规模的数据高效处理要求，借助近邻传播算法的优异性能，通过引入分层增量处理和样本点动态赋权策略，实现了一种能够非常有效地平衡处理效率和采样质量的新方法.其中的分层增量处理策略考虑将原始的大规模数据集进行分批处理后再综合；而样本点动态赋权则考虑在近邻传播过程中对样本点进行合理的动态赋权，以获得采样的数据空间上更好的全局一致性.实验中，分别使用人工数据集、UCI标准数据集和图像数据集进行性能分析，结果表明：新方法与现有相关方法在采样划分质量上可达到同等水平，而计算效率则可实现大幅提升.进一步将新方法应用于深度学习的数据增强任务中，相应的实验结果表明：在原始数据增强方法上结合进高效增量采样处理后，在保持总训练数据集规模的情况下，所获得的模型性能可实现显著的提升. |
| 关键词: 数据采样 近邻传播 动态赋权 增量采样 数据增强 |
| DOI：10.13328/j.cnki.jos.006118 |
| 分类号:TP311 |
| 基金项目:国家自然科学基金（61872166）；江苏省“六大人才高峰”项目（XYDXX-161）；江苏省科技计划（BE2018056） |
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| Incremental Data Sampling Method Using Affinity Propagation with Dynamic Weighting |
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CHEN Xiao-Qi1,2, XIE Zhen-Ping1,2, LIU Yuan1,2, ZHAN Qian-Yi1,2
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1.School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China;2.Jiangsu Key Laboratory of Media Design and Software Technology(Jiangnan University), Wuxi 214122, China
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| Abstract: |
| Data sampling is an important manner to efficiently extract useful information from original huge datasets. In order to fit with the requirements of efficiently dealing with more and more large-scale data, a novel incremental data sampling method originated from affinity propagation method is proposed, in which two integrated algorithm strategies including hierarchical incremental processing and the dynamic weighting of data samples are introduced. The proposed method mainly can balance the computational efficiency and sampling quality very well. For hierarchical incremental processing strategy, it firstly samples data items in batches and then composites samples by hierarchical way. For dynamic weighting of data samples strategy, it dynamically re-weights the preference to retain better global consistency of possible samples on data space in the incremental sampling procedure. In the experiments, artificial datasets, UCI datasets, and image datasets are used to analyze the sampling performance. The experimental results with several compared algorithms indicate that, the proposed method can gain similar sampling quality but with notably higher computational efficiency especially for more large-scale datasets. This study further applies the new method to data augmentation task in deep learning, and the corresponding experimental results show that the proposed method performs excellently. Concretely, if basic training dataset are processed by sampling enhancement with the proposed new method, the trained model performance using similar number of training samples can be significantly improved compared to traditional data enhancement strategies. |
| Key words: data sampling affinity propagation dynamic weighting incremental sampling data augmentation |
