Learning Label Correlations for Multi-Label Online Passive Aggressive Classification Algorithm

Abstract

Label correlations are an essential technique for data mining that solves the possible correlation problem between different labels in multi-label classification. Although this technique is widely used in multi-label classification problems, batch learning deals with most issues, which consumes a lot of time and space resources. Unlike traditional batch learning methods, online learning represents a promising family of efficient and scalable machine learning algorithms for large-scale datasets. However, existing online learning research has done little to consider correlations between labels. On the basis of existing research, this paper proposes a multi-label online learning algorithm based on label correlations by maximizing the interval between related labels and unrelated labels in multi-label samples. We evaluate the performance of the proposed algorithm on several public datasets. Experiments show the effectiveness of our algorithm.

Variational Online Learning Correlation Filter for Visual Tracking

“…However, traditional online learning methods learn only first-order information, which is insufficient because it suffers from overfitting to unconditional appearance variants. The learning algorithm [52] renders the updated classifier similar to the previous classifier and guarantees that the new instance is correctly classified.…”