Computer Science – Learning
Scientific paper
2009-02-08
Computer Science
Learning
Scientific paper
We consider multi-label prediction problems with large output spaces under the assumption of output sparsity -- that the target (label) vectors have small support. We develop a general theory for a variant of the popular error correcting output code scheme, using ideas from compressed sensing for exploiting this sparsity. The method can be regarded as a simple reduction from multi-label regression problems to binary regression problems. We show that the number of subproblems need only be logarithmic in the total number of possible labels, making this approach radically more efficient than others. We also state and prove robustness guarantees for this method in the form of regret transform bounds (in general), and also provide a more detailed analysis for the linear prediction setting.
Hsu Daniel
Kakade Sham M.
Langford J. J.
Zhang Tong
No associations
LandOfFree
Multi-Label Prediction via Compressed Sensing does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Multi-Label Prediction via Compressed Sensing, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Multi-Label Prediction via Compressed Sensing will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-580852