Online Algorithms for Multiclass Classification using Partial Labels
This work addresses classification challenges in scenarios with incomplete labeling, but it is incremental as it extends existing methods like Perceptron and Pegasos.
The paper tackles multiclass classification with partial labels by proposing online algorithms, including variants of Perceptron and Pegasos, and demonstrates their effectiveness through experiments on various datasets.
In this paper, we propose online algorithms for multiclass classification using partial labels. We propose two variants of Perceptron called Avg Perceptron and Max Perceptron to deal with the partial labeled data. We also propose Avg Pegasos and Max Pegasos, which are extensions of Pegasos algorithm. We also provide mistake bounds for Avg Perceptron and regret bound for Avg Pegasos. We show the effectiveness of the proposed approaches by experimenting on various datasets and comparing them with the standard Perceptron and Pegasos.