  Large Deep Neural Networks (DNNs) are often data hungry and need high-quality
labeled data in copious amounts for learning to converge. This is a challenge
in the field of medicine since high quality labeled data is often scarce. Data
programming has been the ray of hope in this regard, since it allows us to
label unlabeled data using multiple weak labeling functions. Such functions are
often supplied by a domain expert. Data-programming can combine multiple weak
labeling functions and suggest labels better than simple majority voting over
the different functions. However, it is not straightforward to express such
weak labeling functions, especially in high-dimensional settings such as images
and time-series data. What we propose in this paper is a way to bypass this
issue, using distance functions. In high-dimensional spaces, it is easier to
find meaningful distance metrics which can generalize across different labeling
tasks. We propose an algorithm that queries an expert for labels of a few
representative samples of the dataset. These samples are carefully chosen by
the algorithm to capture the distribution of the dataset. The labels assigned
by the expert on the representative subset induce a labeling on the full
dataset, thereby generating weak labels to be used in the data programming
pipeline. In our medical time series case study, labeling a subset of 50 to 130
out of 3,265 samples showed 17-28% improvement in accuracy and 13-28%
improvement in F1 over the baseline using clinician-defined labeling functions.
In our medical image case study, labeling a subset of about 50 to 120 images
from 6,293 unlabeled medical images using our approach showed significant
improvement over the baseline method, Snuba, with an increase of approximately
5-15% in accuracy and 12-19% in F1 score.
