Lu Li
ABSTRACT
Real-time human context recognition is one of the most exciting emerging technologies in sensing nowadays. Compared with most recognition problems in machine learning, the challenge lies in the complexity and incompleteness of labels, in other words, each sample can have several label concepts simultaneously but some of them could be missing. This poster proposes an effective approach for multilabel human context recognition with signals from sensors embedded in the wearable devices. The proposed algorithm demonstrates to be very robust to incomplete labels.
- Huang, S.-L., Zhang, L., and Zheng, L. An information-theoretic approach to unsupervised feature selection for high-dimensional data. In Information Theory Workshop (ITW), 2017 IEEE (2017), IEEE, pp. 434--438.Google Scholar
Cross Ref
- Vaizman, Y., Ellis, K., and Lanckriet, G. Recognizing detailed human context in the wild from smartphones and smartwatches. IEEE Pervasive Computing 16, 4 (2017), 62--74.Google ScholarCross Ref
- Vaizman, Y., Weibel, N., and Lanckriet, G. Context recognition in-the-wild: Unified model for multi-modal sensors and multi-label classification. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1, 4 (2018), 168. Google ScholarDigital Library
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