Title: Online Multi-Label Classification: A Label Compression Method,
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.