ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction
in Machine Learning
- URL: http://arxiv.org/abs/2401.05418v1
- Date: Fri, 29 Dec 2023 16:49:30 GMT
- Title: ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction
in Machine Learning
- Authors: Salman Haidri
- Abstract summary: ANALYTiC uses active learning to infer semantic annotations from the trajectories by learning from sets of labeled data.
This study serves as a stepping-stone towards the broader integration of machine learning and visual methods in context of movement data analysis.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- 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.
Related papers
- TrajLearn: Trajectory Prediction Learning using Deep Generative Models [4.097342535693401]
Trajectory prediction aims to estimate an entity's future path using its current position and historical movement data.
To address these challenges, we introduce TrajLearn, a novel model for trajectory prediction.
TrajLearn predicts the next $k$ steps by integrating a customized beam search for exploring multiple potential paths.
arXiv Detail & Related papers (2024-12-30T23:38:52Z) - Oriented Tiny Object Detection: A Dataset, Benchmark, and Dynamic Unbiased Learning [51.170479006249195]
We introduce a new dataset, benchmark, and a dynamic coarse-to-fine learning scheme in this study.
Our proposed dataset, AI-TOD-R, features the smallest object sizes among all oriented object detection datasets.
We present a benchmark spanning a broad range of detection paradigms, including both fully-supervised and label-efficient approaches.
arXiv Detail & Related papers (2024-12-16T09:14:32Z) - Capturing the Temporal Dependence of Training Data Influence [100.91355498124527]
We formalize the concept of trajectory-specific leave-one-out influence, which quantifies the impact of removing a data point during training.
We propose data value embedding, a novel technique enabling efficient approximation of trajectory-specific LOO.
As data value embedding captures training data ordering, it offers valuable insights into model training dynamics.
arXiv Detail & Related papers (2024-12-12T18:28:55Z) - SeMoLi: What Moves Together Belongs Together [51.72754014130369]
We tackle semi-supervised object detection based on motion cues.
Recent results suggest that motion-based clustering methods can be used to pseudo-label instances of moving objects.
We re-think this approach and suggest that both, object detection, as well as motion-inspired pseudo-labeling, can be tackled in a data-driven manner.
arXiv Detail & Related papers (2024-02-29T18:54:53Z) - Enhancing Explainability in Mobility Data Science through a combination
of methods [0.08192907805418582]
This paper introduces a comprehensive framework that harmonizes pivotal XAI techniques.
LIMEInterpretable Model-a-gnostic Explanations, SHAP, Saliency maps, attention mechanisms, direct trajectory visualization, and Permutation Feature (PFI)
To validate our framework, we undertook a survey to gauge preferences and reception among various user demographics.
arXiv Detail & Related papers (2023-12-01T07:09:21Z) - Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling [14.88236554564287]
In this work, we build upon advances in unsupervised learning by incorporating information about the structure of a scene into the training process.
We achieve this by (1) learning depth-feature correlation by spatially correlate the feature maps with the depth maps to induce knowledge about the structure of the scene.
We then implement farthest-point sampling to more effectively select relevant features by utilizing 3D sampling techniques on depth information of the scene.
arXiv Detail & Related papers (2023-09-21T11:47:01Z) - ALP: Action-Aware Embodied Learning for Perception [60.64801970249279]
We introduce Action-Aware Embodied Learning for Perception (ALP)
ALP incorporates action information into representation learning through a combination of optimizing a reinforcement learning policy and an inverse dynamics prediction objective.
We show that ALP outperforms existing baselines in several downstream perception tasks.
arXiv Detail & Related papers (2023-06-16T21:51:04Z) - Reinforcement Learning from Passive Data via Latent Intentions [86.4969514480008]
We show that passive data can still be used to learn features that accelerate downstream RL.
Our approach learns from passive data by modeling intentions.
Our experiments demonstrate the ability to learn from many forms of passive data, including cross-embodiment video data and YouTube videos.
arXiv Detail & Related papers (2023-04-10T17:59:05Z) - Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or
Something Else? [93.91375268580806]
Both politics and pandemics have recently provided ample motivation for the development of machine learning-enabled disinformation (a.k.a. fake news) detection algorithms.
Existing literature has focused primarily on the fully-automated case, but the resulting techniques cannot reliably detect disinformation on the varied topics, sources, and time scales required for military applications.
By leveraging an already-available analyst as a human-in-the-loop, canonical machine learning techniques of sentiment analysis, aspect-based sentiment analysis, and stance detection become plausible methods to use for a partially-automated disinformation detection system.
arXiv Detail & Related papers (2021-11-09T13:30:34Z) - Clustering augmented Self-Supervised Learning: Anapplication to Land
Cover Mapping [10.720852987343896]
We introduce a new method for land cover mapping by using a clustering based pretext task for self-supervised learning.
We demonstrate the effectiveness of the method on two societally relevant applications.
arXiv Detail & Related papers (2021-08-16T19:35:43Z) - Enhancing ensemble learning and transfer learning in multimodal data
analysis by adaptive dimensionality reduction [10.646114896709717]
In multimodal data analysis, not all observations would show the same level of reliability or information quality.
We propose an adaptive approach for dimensionality reduction to overcome this issue.
We test our approach on multimodal datasets acquired in diverse research fields.
arXiv Detail & Related papers (2021-05-08T11:53:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.