Interactive Machine Learning at Scale With CHISSL

Authors

  • Dustin Arendt Pacific Northwest National Laboratory
  • Emily Grace Pacific Northwest National Laboratory
  • Svitlana Volkova Pacific Northwest National Laboratory

Keywords:

semi-supervised learning, user interface, agglomerative clustering

Abstract

We demonstrate CHISSL a scalable client-server system for real-time interactive machine learning. Our system is capable of incorporating user feedback incrementally and immediately without a pre-defined prediction task. Computation is partitioned between a lightweight web-client and a heavyweight server. The server relies on representation learning and off-the-shelf agglomerative clustering to find a dendrogram, which we use to quickly approximate distances in the representation space. The client, using only this dendrogram, incorporates user feedback via transduction. Distances and predictions for each unlabeled instance are updated incrementally and deterministically, with O(n) space and time complexity. Our algorithm is implemented in a functional prototype, designed to be easy to use by non-experts. The prototype organizes the large amounts of data into recommendations. This allows the user to interact with actual instances by dragging and dropping to provide feedback in an intuitive manner. We applied CHISSL to several domains including cyber, social media, and geo-temporal analysis.

Downloads

Published

2018-04-29

How to Cite

Arendt, D., Grace, E., & Volkova, S. (2018). Interactive Machine Learning at Scale With CHISSL. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11370