Machine learning with only positive labels Suppose I have a binary classification problem with 10 features and about 1000 samples. In the training set, most of my data is unlabeled (75%). The rest of the data is labeled but contains only positive labels.

In the test set, I have both negative and positive labels. How should I approach this classification problem?
I would use a novelty detection approach: Use SVMs (one-class) to find a hyperplane around the existing positive samples. Alternatively, you could use GMMs to fit multiple hyper-ellipsoids to enclose the positive examples. Then given a test image, for the case of SVMs, you check whether this falls within the hyperplane or not. For GMMs, you check if it is enclosed in the hyper-ellipsoids. They are both proven to work well in practice.

If you also have some unlabled data in your training set, I would certainly adapt a variant of transfer learning. Maybe you would be able to automatically label the unlabeled data, based on the already learnt samples.