Elliptical summary randomisation for sensor-based human activity recognition
Erfani, Sarah, Baktashmotlagh, Mahsa, Moshtaghi, Masoud, Leckie, Chris, Bailey, James, & Kotagiri, Ramamohanarao (2016) Elliptical summary randomisation for sensor-based human activity recognition. In 25th International Joint Conference on Artificial Intelligence (IJCAI-16), 9-15 July 2016, New York City. (Unpublished)
Many conventional statistical machine learning al- gorithms generalise poorly if distribution bias ex- ists in the datasets. For example, distribution bias arises in the context of domain generalisation, where knowledge acquired from multiple source domains need to be used in a previously unseen target domains. We propose Elliptical Summary Randomisation (ESRand), an efficient domain generalisation approach that comprises of a randomised kernel and elliptical data summarisation. ESRand learns a domain interdependent projection to a la- tent subspace that minimises the existing biases to the data while maintaining the functional relationship between domains. In the latent subspace, ellipsoidal summaries replace the samples to enhance the generalisation by further removing bias and noise in the data. Moreover, the summarisation enables large-scale data processing by significantly reducing the size of the data. Through comprehensive analysis, we show that our subspace-based approach outperforms state-of-the-art results on several activity recognition benchmark datasets, while keeping the computational complexity significantly low.
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|Item Type:||Conference Paper|
|Divisions:||Current > QUT Faculties and Divisions > Science & Engineering Faculty|
|Copyright Owner:||Copyright 2016 The Author(s)|
|Deposited On:||29 Mar 2016 01:57|
|Last Modified:||15 Jul 2016 14:00|
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