Given a binarily labeled train set, and an unlabeled test set, consider the following two-step classification system:
- step 1: the train and test data is clustered.
- step 2: an SVM is fitted for each cluster using the train data and train labels only. Then, if a test data belong to cluster X, we use SVM of cluster X to determine its label.
Can one call such a classification system transductive SVM?
From my understanding: on one side, there is transduction since the unlabeled test set is used in the training phase (to find the clusters). But on the other side, I thought transductive SVM was specifically referring to another kind of classification system, where no clustering was involved (only SVM margins).