Oregon State University

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Event Details

PhD Oral Preliminary Examination – Xinze Guan

Monday, March 7, 2016 1:00 PM - 3:00 PM

Graphical Models for Multiple Instance Learning from Time Series Data with Applications to Activity Recognition
Activity recognition from sensor data has spurred a great deal of interest due to its impact on health care and security. Previous work on activity recognition from multivariate time series data has mainly applied supervised learning techniques which require a high degree of annotation effort to produce training data with the start and end times of each activity. Multi-instance learning (MIL) and multi-instance multi-label learning (MIML) offer an alternative to standard supervised learning in the form of weak supervision, in which ambiguity in the labeling can reduce the annotation effort in producing labeled training data. We introduce generative graphical models for MIL and MIML based on auto-regressive processes. Our first work, based on a mixture of auto-regressive processes, assumed that instances within a bag were independent. We then relaxed the i.i.d assumption for instances and extended the model by considering the sequential structure within a bag. Finally we take a MIML approach to predict the presence of multiple activities within a time interval. With our MIML approach, we propose to discover novel activities that are not modeled by our set of labels.

Major Advisor: Weng-Keen Wong
Committee: Raviv Raich
Committee: Alan Fern
Committee: Prasad Tadepalli
GCR: Cindy Grimm

Kelley Engineering Center (campus map)
Nicole Thompson
1 541 737 3617
Nicole.Thompson at oregonstate.edu
Sch Elect Engr/Comp Sci
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