基于被动-主动的特征演化流学习

刘艳芳; 李文斌; 高阳

doi:10.7544/issn1000-1239.2021.20210330

基于被动-主动的特征演化流学习

¹(计算机软件新技术国家重点实验室(南京大学) 南京 210023)
²(龙岩学院数学与信息工程学院福建龙岩 364012) (liuyanfang003@163.com)

基金项目: 国家重点研发计划项目(2018AAA0100905)；福建省中青年教师教育科研项目(科技类)(JAT190743)；龙岩市科技计划项目(2019LYF13002,2019LYF12010)

详细信息

中图分类号: TP391
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出版历程
- 发布日期: 2021-07-31

Passive-Aggressive Learning with Feature Evolvable Streams

¹(State Key Laboratory for Novel Software Technology (Nanjing University), Nanjing 210023)
²(College of Mathematics and Information Engineering, Longyan University, Longyan, Fujian 364012)

Funds: This work was supported by the National Key Research and Development Program of China (2018AAA0100905), the Education Scientific Research Project of Young Teachers of Fujian Province (JAT190743), and the Science and Technology Project of Longyan City (2019LYF13002, 2019LYF12010).

摘要

摘要: 在许多现实应用中，数据以一种特征演化流的形式收集.例如，随着传感器的更换，由旧传感器收集的数据特征会消失，新传感器收集的数据特征会出现.在线被动-主动算法已被证明可以有效地从具有固定特征空间和梯形特征空间的数据集中学习线性分类器.因此，提出了一种基于被动-主动更新策略的特征演化学习算法(passive-aggressive learning with feature evolvable streams, PAFE).该算法通过主动-被动更新策略从当前特征空间和被恢复的已消失特征空间中学习了2个模型.具体来说，在重叠时段，即新旧特征同时存在的时段，该算法用新特征恢复了消失的特征空间，同时用旧特征空间模拟了新特征空间，进而为新特征空间的模型学习提供合理的初始化.基于这2个模型，为提高算法整体性能提出了2个集成算法：组合预测和当前最优预测.在合成数据集和真实数据集上的实验结果验证了该算法的有效性.
- 在线学习 /
- 被动-主动策略 /
- 监督学习 /
- 集成学习 /
- 演化特征
Abstract: In many real-world applications, data are collected in the form of a feature evolvable stream. For instance, old features of data gathered by limited-lifespan sensors disappear and new features emerge at the same time along with the sensors exchanging simultaneously. Online passive-aggressive algorithms have proven to be effective in learning linear classifiers from datasets with both a fixed feature space and a trapezoidal feature space. Therefore, in this paper we propose a new feature evolvable learning based on passive-aggressive update strategy (PAFE), which utilizes the margin to modify the current classifier. The proposed algorithm learns two models through passive-aggressive update strategy from the current features and recovered features of the vanished features. Specifically, we both recover the vanished features and mine the initialization of the current model from the overlapping periods in which both old and new features are available. Furthermore, we use two ensemble methods to improve performance: combining the predictions from the two models, and dynamically selecting the best single prediction. Experiments on both synthetic and real data validate the effectiveness of our proposed algorithm.
- online learning /
- passive-aggressive strategy /
- supervised learning /
- ensemble learning /
- evolvable features

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1.	黄翔东，陈红红，甘霖. 基于频率-时间扩张密集网络的语音增强方法. 计算机研究与发展. 2023(07): 1628-1638 . 本站查看
2.	许春冬，徐琅，周滨. 结合优化U-Net和残差神经网络的单通道语音增强算法. 现代电子技术. 2022(09): 35-40 . 百度学术
3.	葛宛营，张天骐，范聪聪，张天. 噪声情况下采用稀疏非负矩阵分解与深度吸引子网络的人声分离算法. 声学学报. 2021(01): 55-66 . 百度学术
4.	GE Wanying，ZHANG Tianqi，FAN Congcong，ZHANG Tian. Monaural noisy speech separation combining sparse non-negative matrix factorization and deep attractor network. Chinese Journal of Acoustics. 2021(02): 266-280 . 必应学术
5.	王静红，梁丽娜，李昊康，周易. 基于注意力网络特征的社区发现算法. 山东大学学报(理学版). 2021(09): 1-12+20 . 百度学术
6.	张天骐，柏浩钧，叶绍鹏，刘鉴兴. 基于门控残差卷积编解码网络的单通道语音增强方法. 信号处理. 2021(10): 1986-1995 . 百度学术
7.	曹丽静. 语音增强技术研究综述. 河北省科学院学报. 2020(02): 30-36 . 百度学术
8.	张天骐，张晓艳，周琳，胡延平. 基于稀疏性的相位谱补偿语音增强算法. 信号处理. 2020(11): 1867-1876 . 百度学术
9.	时文华，张雄伟，邹霞，孙蒙. 利用深度全卷积编解码网络的单通道语音增强. 信号处理. 2019(04): 631-640 . 百度学术