Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Grafting: fast, incremental feature selection by gradient descent in function space
The Journal of Machine Learning Research
Feature selection, L1 vs. L2 regularization, and rotational invariance
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A shallow model of backchannel continuers in spoken dialogue
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Natural behavior of a listening agent
Lecture Notes in Computer Science
Predicting Listener Backchannels: A Probabilistic Multimodal Approach
IVA '08 Proceedings of the 8th international conference on Intelligent Virtual Agents
ICMI '08 Proceedings of the 10th international conference on Multimodal interfaces
Social signal processing: state-of-the-art and future perspectives of an emerging domain
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Social signal processing: Survey of an emerging domain
Image and Vision Computing
A spoken dialog system for chat-like conversations considering response timing
TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
Efficiently inducing features of conditional random fields
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Regularisation techniques for conditional random fields: parameterised versus parameter-free
IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing
Challenges of human behavior understanding
HBU'10 Proceedings of the First international conference on Human behavior understanding
Modeling wisdom of crowds using latent mixture of discriminative experts
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
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One of the key challenge in social behavior analysis is to automatically discover the subset of features relevant to a specific social signal (e.g., backchannel feedback). The way that these social signals are performed exhibit some variations among different people. In this paper, we present a feature selection approach which first looks at important behaviors for each individual, called self-features, before building a consensus. To enable this approach, we propose a new feature ranking scheme which exploits the sparsity of probabilistic models when trained on human behavior problems. We validated our self-feature concensus approach on the task of listener backchannel prediction and showed improvement over the traditional group-feature approach. Our technique gives researchers a new tool to analyze individual differences in social nonverbal communication.