Online Multi-Label Classification: A Label Compression Method
This work addresses the problem of efficient multi-label classification for applications like gene categorization and image labeling, offering a practical solution for real-world scenarios with incremental improvements.
The paper tackles the challenge of online multi-label classification with many labels by proposing a fast linear label compression method that reduces label space dimensionality and trains models on encoded pseudo labels, achieving improved running times and prediction performance across various measures.
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.