Lu, X;
Qiao, Y;
Zhu, R;
Wang, G;
Ma, Z;
Xue, J-H;
(2021)
Generalisations of stochastic supervision models.
Pattern Recognition
, 109
, Article 107575. 10.1016/j.patcog.2020.107575.
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Abstract
When the labelling information is not deterministic, traditional supervised learning algorithms cannot be applied. In this case, stochastic supervision models provide a valuable alternative to classification. However, these models are restricted in several aspects, which critically limits their applicability. In this paper, we provide four generalisations of stochastic supervision models, extending them to asymmetric assessments, multiple classes, feature-dependent assessments and multi-modal classes, respectively. Corresponding to these generalisations, we derive four new EM algorithms. We show the effectiveness of our generalisations through illustrative examples of simulated datasets, as well as real-world examples of three famous datasets, the MNIST dataset, the CIFAR-10 dataset and the EMNIST dataset.
| Type: | Article |
|---|---|
| Title: | Generalisations of stochastic supervision models |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.patcog.2020.107575 |
| Publisher version: | http://dx.doi.org/10.1016/j.patcog.2020.107575 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | EM algorithms, Imperfect supervision, Finite mixture model, Stochastic supervision |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10107957 |
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