Semi-supervised multi-label classification using an extended graph-based manifold regularization

Li Ding

doi:10.1007/s40747-021-00611-7

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Semi-supervised multi-label classification using an extended graph-based manifold regularization

2022-01-04

Li Ding *

Scott Dick

DOI：10.1007/s40747-021-00611-7

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Abstract

En 中文

Graph-based algorithms are known to be effective approaches to semi-supervised learning. However, there has been relatively little work on extending these algorithms to the multi-label classification case. We derive an extension of the Manifold Regularization algorithm to multi-label classification, which is significantly simpler than the general Vector Manifold Regularization approach. We then augment our algorithm with a weighting strategy to allow differential influence on a model between instances having ground-truth vs. induced labels. Experiments on four benchmark multi-label data sets show that the resulting algorithm performs better overall compared to the existing semi-supervised multi-label classification algorithms at various levels of label sparsity. Comparisons with state-of-the-art supervised multi-label approaches (which of course are fully labeled) also show that our algorithm outperforms all of them even with a substantial number of unlabeled examples.

Keywords:

Manifold regularization

Multi-label classification

Semi-supervised learning

Graph-based learning

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