Abstract
The goal of semi-supervised learning is to improve supervised classifiers by using additional unlabeled training examples. In this work we study a simple self-learning approach to semi-supervised learning applied to the least squares classifier. We show that a soft-label and a hard-label variant of self-learning can be derived by applying block coordinate descent to two related but slightly different objective functions. The resulting soft-label approach is related to an idea about dealing with missing data that dates back to the 1930s. We show that the soft-label variant typically outperforms the hard-label variant on benchmark datasets and partially explain this behaviour by studying the relative difficulty of finding good local minima for the corresponding objective functions.
Original language | English |
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Title of host publication | 2016 23rd International Conference on Pattern Recognition (ICPR) |
Publisher | IEEE |
Pages | 1677-1682 |
Number of pages | 6 |
ISBN (Electronic) | 978-1-5090-4847-2 |
ISBN (Print) | 978-1-5090-4848-9 |
DOIs | |
Publication status | Published - 2016 |
Event | ICPR 2016: 23rd International Conference on Pattern Recognition - Cancún, Mexico Duration: 4 Dec 2016 → 8 Dec 2016 Conference number: 23 |
Conference
Conference | ICPR 2016 |
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Country/Territory | Mexico |
City | Cancún |
Period | 4/12/16 → 8/12/16 |
Keywords
- Linear programming
- Semisupervised learning
- Labeling
- Training
- Encoding
- Optimization
- Convergence