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

作者：Hachiya Hirotaka^*; Sugiyama Masashi; Ueda Naonori

来源：Neurocomputing, 2012, 80: 93-101.

DOI：10.1016/j.neucom.2011.09.016

• Human activity recognition
• Semi-supervised learning
• Probabilistic classifier
• Covariate shift
• Importance sampling

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 cornputationally efficient probabilistic classification method based on adaptive importance sampling. The usefulness of the proposed method is demonstrated in real-world human activity recognition.

• 出版日期2012-3-15