Multi-Decoder RNN Autoencoder Based on Variational Bayes Method
| Autor: | Masahiro Kobayashi, Kazuho Watanabe, Daisuke Kaji |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Feature extraction Machine Learning (stat.ML) 02 engineering and technology Statistics - Applications Machine Learning (cs.LG) 03 medical and health sciences Bayes' theorem 0302 clinical medicine Statistics - Machine Learning Robustness (computer science) 0202 electrical engineering electronic engineering information engineering Applications (stat.AP) Time series Cluster analysis Artificial neural network business.industry Pattern recognition Autoencoder Generative model Recurrent neural network 020201 artificial intelligence & image processing Artificial intelligence business 030217 neurology & neurosurgery |
| Zdroj: | IJCNN |
| DOI: | 10.1109/ijcnn48605.2020.9206686 |
| Popis: | 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. 8 pages, 11 figures, accepted for publication in IJCNN |
| Databáze: | OpenAIRE |
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