论文标题
基于变异贝叶斯方法
Multi-Decoder RNN Autoencoder Based on Variational Bayes Method
论文作者
论文摘要
聚类算法具有广泛的应用程序，并在包括时间序列数据分析在内的数据分析字段中起重要作用。但是，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藏变量的初始值。基于时间序列的生成模型，几乎没有讨论过这些方法。在本文中，我们提出了一种新的聚类算法，该算法的重点是具有复发性神经网络和变异贝叶斯方法的信号的生成过程。我们的实验表明，所提出的算法不仅具有针对相移，振幅和信号长度变化的鲁棒性，而且还基于变异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.
