Few days back I first heard about the semi-supervised learning from this amazing book called "Introduction to statistical learning". So, I started some digging. Gathered some idea about semi-supervised learning. Unfortunately, all of my findings had discussed semi-supervised learning with customised or unrealistic dataset. Or just explained the concept.
Can anyone please give me a REAL WORLD dataset example where semi-supervised learning is applicable?
Semi supervised means: some of the data could be labelled and some unlabeled.
You may have a look on publicly available real world data here:
https://www.robots.ox.ac.uk/~vgg/data/
https://www.robots.ox.ac.uk/~vgg/data/
In semi-supervised learning we try to leverage information from both labeled and unlabeled data to improve prediction performance. This is particularly useful when we have more unlabeled than labeled data and when the cost of labeling data (economic and time investment) is high.
For example, in the problem of Automatic Prediction of Protein Function (AFP) we are exactly in this situation. We have few labeled proteins for each function (e.g. GO term) and we want to use the entire protein network to make predictions. Labeling new proteins requires time to perform experimental validation and, even if we have high-throughput technologies we are far to fill the gap.
Hope it helps :)
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