Composable Core-sets for Diversity Approximation on Multi-Dataset Streams
Core-sets refer to subsets of data that maximize some function that is
commonly a diversity or group requirement. These subsets are used in place of
the original data to accomplish a given task with comparable or even enhanced
performance if biases are removed. Composable core-sets are core-sets with the
property that subsets of the core set can be unioned together to obtain an
approximation for the original data; lending themselves to be used for streamed
or distributed data. Recent work has focused on the use of core-sets for
training machine learning models. Preceding solutions such as CRAIG have been
proven to approximate gradient descent while providing a reduced training time.
In this paper, we introduce a core-set construction algorithm for constructing
composable core-sets to summarize streamed data for use in active learning
environments. If combined with techniques such as CRAIG and heuristics to
enhance construction speed, composable core-sets could be used for real time
training of models when the amount of sensor data is large. We provide
empirical analysis by considering extrapolated data for the runtime of such a
brute force algorithm. This algorithm is then analyzed for efficiency through
averaged empirical regression and key results and improvements are suggested
for further research on the topic.