ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning

TL;DR

This paper introduces ANALYTiC, a method combining dimensionality reduction, decision boundaries, and active learning to improve trajectory data analysis and labeling accuracy across multiple datasets.

Contribution

It explores the integration of dimensionality reduction and decision boundaries with active learning for enhanced trajectory data interpretation.

Findings

Improved efficiency in trajectory labeling.

Enhanced interpretability of movement data.

Potential for broader machine learning integration.

Abstract

The advent of compact, handheld devices has given us a pool of tracked movement data that could be used to infer trends and patterns that can be made to use. With this flooding of various trajectory data of animals, humans, vehicles, etc., the idea of ANALYTiC originated, using active learning to infer semantic annotations from the trajectories by learning from sets of labeled data. This study explores the application of dimensionality reduction and decision boundaries in combination with the already present active learning, highlighting patterns and clusters in data. We test these features with three different trajectory datasets with objective of exploiting the the already labeled data and enhance their interpretability. Our experimental analysis exemplifies the potential of these combined methodologies in improving the efficiency and accuracy of trajectory labeling. This study serves as a stepping-stone towards the broader integration of machine learning and visual methods in context of movement data analysis.