Composable Core-sets for Diversity Approximation on Multi-Dataset Streams

TL;DR

This paper introduces a new algorithm for creating composable core-sets that summarize streamed data, enabling efficient real-time model training in active learning environments, especially with large sensor datasets.

Contribution

The paper presents a novel core-set construction algorithm for streaming data, enhancing the efficiency of diversity approximation for active learning and model training.

Findings

Empirical analysis shows the algorithm's runtime performance.

Improvements over previous methods are suggested for future research.

Potential for real-time training with large sensor data streams.

Abstract

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.