Unsupervised feature learning for electronic nose data applied to Bacteria Identification in Blood.
2011 (English)Conference paper, Poster (Refereed)
Electronic nose (e-nose) data represents multivariate time-series from an array of chemical gas sensors exposed to a gas. This data is a new data set for usewith deep learning methods, and is highly suitable since e-nose data is complexand difficult to interpret for human experts. Furthermore, this data set presentsa number of interesting challenges for deep learning architectures per se. In this work we present a first study of e-nose data classification using deep learningwhen testing for the presence of bacteria in blood and agar solutions. We showin this study that deep learning outperforms hand-selected strategy based methods which has been previously tried with the same data set.
Place, publisher, year, edition, pages
Engineering and Technology
Research subject Computer and Systems Science
IdentifiersURN: urn:nbn:se:oru:diva-24197OAI: oai:DiVA.org:oru-24197DiVA: diva2:542614
NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning