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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Detalles Bibliográficos
Autores principales: Nagano, Masatoshi, Nakamura, Tomoaki, Nagai, Takayuki, Mochihashi, Daichi, Kobayashi, Ichiro, Takano, Wataru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Robotics and AI
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805757/
https://www.ncbi.nlm.nih.gov/pubmed/33501130
http://dx.doi.org/10.3389/frobt.2019.00115

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7805757/
https://www.ncbi.nlm.nih.gov/pubmed/33501130
http://dx.doi.org/10.3389/frobt.2019.00115

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