HVGH: Unsupervised Segmentation for High-Dimensional Time Series Using Deep Neural Compression and Statistical Generative Model

HVGH: Unsupervised Segmentation for High-Dimensional Time Series Using Deep Neural Compression and Statistical Generative Model

Humans perceive continuous high-dimensional information by dividing it into meaningful segments, such as words and units of motion. We believe that such unsupervised segmentation is also important for robots to learn topics such as language and motion. To this end, we previously proposed a hierarchi...

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Bibliographic Details
Main Authors: Masatoshi Nagano, Tomoaki Nakamura, Takayuki Nagai, Daichi Mochihashi, Ichiro Kobayashi, Wataru Takano
Format: Article
Language:English
Published: Frontiers Media S.A. 2019-11-01
Series:Frontiers in Robotics and AI
Subjects:
motion segmentation
Gaussian process
variational autoencoder
hidden semi-Markov model
motion capture data
high-dimensional time-series data
Online Access:https://www.frontiersin.org/article/10.3389/frobt.2019.00115/full

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https://www.frontiersin.org/article/10.3389/frobt.2019.00115/full

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