Let's assume that one has the following inputs and corresponding outputs:

x1 = (a,b,c) with corresponding output y1, which is a number.
x2 = (d,e,?) with unknown output
x3 = (?,?,g) with known output y2, which is a number

and so on.

So, by "incomplete input data" I don't just mean that some input data is unlabeled but some data-points from certain data-vectors are missing and are sometimes labeled and sometimes unlabeled.

Is there at least one known online semi-supervised learning algorithm for this kind of "input data with gaps"?

So far I only found algorithms for online semi-supervised learning for "input data without any gaps".


1 Answer 1


I came across a new paper that does just this, however I have not read it carefully: Max-Ordinal Learning.

Another option is the E-M algorithm, which is commonly used with missing data.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.