Digital signal processing (3rd ed.): principles, algorithms, and applications
Digital signal processing (3rd ed.): principles, algorithms, and applications
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Name-It: Naming and Detecting Faces in News Videos
IEEE MultiMedia
Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
The Journal of Machine Learning Research
The mathematics of statistical machine translation: parameter estimation
Computational Linguistics - Special issue on using large corpora: II
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Statistical Analysis of Some Multi-Category Large Margin Classification Methods
The Journal of Machine Learning Research
A Discriminative Learning Framework with Pairwise Constraints for Video Object Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Movie/Script: Alignment and Parsing of Video and Text Transcription
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Learning from ambiguously labeled examples
Intelligent Data Analysis - Selected papers from IDA2005, Madrid, Spain
Introduction to Semi-Supervised Learning
Introduction to Semi-Supervised Learning
Estimating Labels from Label Proportions
The Journal of Machine Learning Research
Semi-Supervised Learning
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
On the consistency of multiclass classification methods
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Partially supervised learning by a credal EM approach
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Facing scalability: Naming faces in an online social network
Pattern Recognition
Rank-loss support instance machines for MIML instance annotation
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Probability estimation for multi-class classification based on label ranking
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Instance Annotation for Multi-Instance Multi-Label Learning
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on ACM SIGKDD 2012
3D Wikipedia: using online text to automatically label and navigate reconstructed geometry
ACM Transactions on Graphics (TOG)
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We address the problem of partially-labeled multiclass classification, where instead of a single label per instance, the algorithm is given a candidate set of labels, only one of which is correct. Our setting is motivated by a common scenario in many image and video collections, where only partial access to labels is available. The goal is to learn a classifier that can disambiguate the partially-labeled training instances, and generalize to unseen data. We define an intuitive property of the data distribution that sharply characterizes the ability to learn in this setting and show that effective learning is possible even when all the data is only partially labeled. Exploiting this property of the data, we propose a convex learning formulation based on minimization of a loss function appropriate for the partial label setting. We analyze the conditions under which our loss function is asymptotically consistent, as well as its generalization and transductive performance. We apply our framework to identifying faces culled from web news sources and to naming characters in TV series and movies; in particular, we annotated and experimented on a very large video data set and achieve 6% error for character naming on 16 episodes of the TV series Lost.