The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Using GPS to learn significant locations and predict movement across multiple users
Personal and Ubiquitous Computing
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
WICON '06 Proceedings of the 2nd annual international workshop on Wireless internet
IEEE Pervasive Computing
Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Experience sampling for building predictive user models: a comparative study
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Activity sensing in the wild: a field trial of ubifit garden
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Understanding mobility based on GPS data
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
UbiGreen: investigating a mobile tool for tracking and supporting green transportation habits
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
Cooperative Techniques Supporting Sensor-Based People-Centric Inferencing
Pervasive '08 Proceedings of the 6th International Conference on Pervasive Computing
Proceedings of the 7th international conference on Mobile systems, applications, and services
SoundSense: scalable sound sensing for people-centric applications on mobile phones
Proceedings of the 7th international conference on Mobile systems, applications, and services
Exploring semi-supervised and active learning for activity recognition
ISWC '08 Proceedings of the 2008 12th IEEE International Symposium on Wearable Computers
A hybrid discriminative/generative approach for modeling human activities
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
CrowdSearch: exploiting crowds for accurate real-time image search on mobile phones
Proceedings of the 8th international conference on Mobile systems, applications, and services
EmotionSense: a mobile phones based adaptive platform for experimental social psychology research
Proceedings of the 12th ACM international conference on Ubiquitous computing
The Jigsaw continuous sensing engine for mobile phone applications
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
A Large Scale Gathering System for Activity Data with Mobile Sensors
ISWC '11 Proceedings of the 2011 15th Annual International Symposium on Wearable Computers
Proceedings of the 13th international conference on Ubiquitous computing
Exploiting Social Networks for Large-Scale Human Behavior Modeling
IEEE Pervasive Computing
Hybrid SN: Interlinking Opportunistic and Online Communities to Augment Information Dissemination
UIC-ATC '12 Proceedings of the 2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing
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Sensor-enabled smartphones are opening a new frontier in the development of mobile sensing applications. The recognition of human activities and context from sensor data using classification models underpins these emerging applications. However, conventional approaches to training classifiers struggle to cope with the diverse user populations routinely found in large-scale popular mobile applications. Differences between users (e.g., age, sex, behavioral patterns, lifestyle) confuse classifiers, which assume everyone is the same. To address this, we propose Community Similarity Networks (CSN), which incorporates inter-person similarity measurements into the classifier training process. Under CSN, every user has a unique classifier that is tuned to their own characteristics. CSN exploits crowd-sourced sensor data to personalize classifiers with data contributed from other similar users. This process is guided by similarity networks that measure different dimensions of inter-person similarity. Our experiments show CSN outperforms existing approaches to classifier training under the presence of population diversity.