Importance-weighted least-squares probabilistic classifier for covariate shift adaptation with application to human activity recognition

Authors:
Hirotaka Hachiya;Masashi Sugiyama;Naonori Ueda
Affiliations:
Tokyo Institute of Technology, Tokyo, Japan;Tokyo Institute of Technology, Tokyo, Japan;NTT Communication Science Laboratories, Kyoto, Japan
Venue:
Neurocomputing
Year:
2012

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Abstract

Human activity recognition from accelerometer data (e.g., obtained by smart phones) is gathering a great deal of attention since it can be used for various purposes such as remote health-care. However, since collecting labeled data is bothersome for new users, it is desirable to utilize data obtained from existing users. In this paper, we formulate this adaptation problem as learning under covariate shift, and propose a computationally efficient probabilistic classification method based on adaptive importance sampling. The usefulness of the proposed method is demonstrated in real-world human activity recognition.