Streaming Label Learning for Modeling Labels on the Fly
This addresses the challenge of dynamic label sets in multi-label learning for applications in changing environments, but it is incremental as it builds on existing multi-label learning frameworks.
The paper tackles the problem of handling a large volume of labels in multi-label learning when labels arrive on the fly in changing environments, and results show that the proposed Streaming Label Learning (SLL) method effectively handles new labels and provides excellent classification performance.
It is challenging to handle a large volume of labels in multi-label learning. However, existing approaches explicitly or implicitly assume that all the labels in the learning process are given, which could be easily violated in changing environments. In this paper, we define and study streaming label learning (SLL), i.e., labels are arrived on the fly, to model newly arrived labels with the help of the knowledge learned from past labels. The core of SLL is to explore and exploit the relationships between new labels and past labels and then inherit the relationship into hypotheses of labels to boost the performance of new classifiers. In specific, we use the label self-representation to model the label relationship, and SLL will be divided into two steps: a regression problem and a empirical risk minimization (ERM) problem. Both problems are simple and can be efficiently solved. We further show that SLL can generate a tighter generalization error bound for new labels than the general ERM framework with trace norm or Frobenius norm regularization. Finally, we implement extensive experiments on various benchmark datasets to validate the new setting. And results show that SLL can effectively handle the constantly emerging new labels and provides excellent classification performance.