# ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning

arXiv: https://arxiv.org/abs/2401.05418 · HTML: https://mlsanity.com/paper/2401.05418/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 33 references that are in the map.
- First posted on arXiv 29 Dec 2023.

## Builds on (most cited first, 7 of 7)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning](https://mlsanity.com/paper/1811.12808/) (2018)
- [A Tutorial on Principal Component Analysis](https://mlsanity.com/paper/1404.1100/) (2014)
- [A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vessels](https://mlsanity.com/paper/2207.05514/) (2022)
- [PTRAIL -- A python package for parallel trajectory data preprocessing](https://mlsanity.com/paper/2108.13202/) (2021)
- [Wise Sliding Window Segmentation: A classification-aided approach for trajectory segmentation](https://mlsanity.com/paper/2003.10248/) (2020)

## Abstract (arXiv)

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.