6533b7d5fe1ef96bd1264f3a
RESEARCH PRODUCT
A label compression method for online multi-label classification
Stefan KramerZahra Ahmadisubject
Multi-label classificationCurrent (mathematics)business.industryComputer sciencePattern recognition02 engineering and technologySpace (commercial competition)Compression methodTask (project management)Reduction (complexity)ComputingMethodologies_PATTERNRECOGNITIONArtificial Intelligence020204 information systemsSignal Processing0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligencebusinessSoftwaredescription
Abstract Many modern applications deal with multi-label data, such as functional categorizations of genes, image labeling and text categorization. Classification of such data with a large number of labels and latent dependencies among them is a challenging task, and it becomes even more challenging when the data is received online and in chunks. Many of the current multi-label classification methods require a lot of time and memory, which make them infeasible for practical real-world applications. In this paper, we propose a fast linear label space dimension reduction method that transforms the labels into a reduced encoded space and trains models on the obtained pseudo labels. Additionally, it provides an analytical method to update the decoding matrix which maps the labels into the original space and is used during the test phase. Experimental results show the effectiveness of this approach in terms of running times and the prediction performance over different measures.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-08-01 | Pattern Recognition Letters |