Multi-Decoder RNN Autoencoder Based on Variational Bayes Method
Multi-Decoder RNN Autoencoder Based on Variational Bayes Method
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DOI：
10.1109/ijcnn48605.2020.9206686
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
Daisuke Kaji;Kazuho Watanabe;Masahiro Kobayashi
中科院分区：
文献类型：
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作者：
Daisuke Kaji;Kazuho Watanabe;Masahiro Kobayashi
聚类算法有着广泛的应用，在包括时间序列数据分析在内的数据分析领域发挥着重要作用。然而，在时间序列分析中，大多数算法都使用信号形状特征或神经网络隐变量的初始值。关于基于时间序列生成模型的方法的讨论很少。在本文中，我们提出了一种新的聚类算法，重点关注循环神经网络和变分贝叶斯方法的信号生成过程。我们的实验表明，所提出的算法不仅对相移、幅度和信号长度变化具有鲁棒性，而且基于变分贝叶斯方法的特性提供了灵活的聚类。
Clustering algorithms have wide applications and play an important role in data analysis fields including time series data analysis. However, in time series analysis, most of the algorithms used signal shape features or the initial value of hidden variable of a neural network. Little has been discussed on the methods based on the generative model of the time series. In this paper, we propose a new clustering algorithm focusing on the generative process of the signal with a recurrent neural network and the variational Bayes method. Our experiments show that the proposed algorithm not only has a robustness against for phase shift, amplitude and signal length variations but also provide a flexible clustering based on the property of the variational Bayes method.