Evaluating text categorization
HLT '91 Proceedings of the workshop on Speech and Natural Language
A comparison of classifiers and document representations for the routing problem
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems
Theoretical Computer Science
Text Categorization Based on Regularized Linear Classification Methods
Information Retrieval
The Journal of Machine Learning Research
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Correlated Label Propagation with Application to Multi-label Learning
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large-scale sparse logistic regression
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-label dimensionality reduction via dependence maximization
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
NUS-WIDE: a real-world web image database from National University of Singapore
Proceedings of the ACM International Conference on Image and Video Retrieval
Image annotation by semantic sparse recoding of visual content
Proceedings of the 20th ACM international conference on Multimedia
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Image annotation aims at finding suitable multiple tags for unlabeled images. Image annotation could be taken as a process of modeling the relationships between images and annotated key words. In this paper, we utilize sparse logistic regression to encode the association between low level visual features and annotated key words for image annotation. The comparisons are made on real data sets in terms of AUC and F1-measure. The results show that sparse logistic regression outperforms other methods substantially almost all the time.