I'm working on a clustering algorithm which assigns each data point an index encoding its cluster. Index permutation is irrelevant to the correctness of the result. The algorithm is self-learning, in that it doesn't require labelled data to achieve its task.
The learning process can be sped up by initially providing labelled data, where each sample is assigned to a specific cluster via its index. Of course, prior to learning, we can only assume the existence of some specific clusters which may actually differ in position, shape and number. Thus, the labels are allowed to be wrong. Even if all of them are wrong, the algorithm will eventually still converge to a correct solution, but it will require a greater number of samples until it does. As long as the majority of labels are correct, there will be a measurable speedup. So the working assumption is that not all, but a sufficient portion of the labelled samples initially fed into the learner, are labelled correctly. But really, there is no guarantee. It's a mere heuristic. Neither the possibility that all labels are correct, nor that all labels are incorrect, can be excluded.
Now, I am unsure about how to describe this concept. Initially, I thought: The speedup is due to 'prior knowledge'. But I find that this term doesn't capture the uncertainty aspect of the concept very well. Personally, I'd prefer something like 'potentially flawed assumptions', but that seems a bit too vague and unwieldy. Is there an established term for this kind of concept in machine learning? If not, what would be an appropriate term which is compatible with existing terminology?