Fast discovery of multidimensional subsequences for robust trajectory classification

• URL: http://arxiv.org/abs/2102.04781v1
• Date: Tue, 9 Feb 2021 11:54:33 GMT
• Title: Fast discovery of multidimensional subsequences for robust trajectory classification
• Authors: Tarlis Portela, Jonata Tyska, Vania Bogorny
• Abstract summary: Trajectory classification tasks became more complex as large volumes of mobility data are being generated every day. Fast classification algorithms are essential for discovering knowledge in trajectory data for real applications. We propose a method for fast discovery of subtrajectories with the reduction of the search space and the optimization of the MASTERMovelets method.
• Score: 0.2578242050187029
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trajectory classification tasks became more complex as large volumes of mobility data are being generated every day and enriched with new sources of information, such as social networks and IoT sensors. Fast classification algorithms are essential for discovering knowledge in trajectory data for real applications. In this work we propose a method for fast discovery of subtrajectories with the reduction of the search space and the optimization of the MASTERMovelets method, which has proven to be effective for discovering interpretable patterns in classification problems.

Related papers

• Optimizing News Text Classification with Bi-LSTM and Attention Mechanism for Efficient Data Processing [4.523790140313845]
  This paper proposes an automaticclassification scheme for news texts based on deep learning. It achieves efficient classification and management of news texts by introducing advanced machine learning algorithms. It has important practical significance for improving the information processing capabilities of the news industry.
  arXiv  Detail & Related papers  (2024-09-23T22:23:08Z)
• Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond [58.63558696061679]
  Trajectory computing is crucial in various practical applications such as location services, urban traffic, and public safety. We present a review of development and recent advances in deep learning for trajectory computing (DL4Traj) Notably, we encapsulate recent advancements in Large Language Models (LLMs) that hold potential to augment trajectory computing.
  arXiv  Detail & Related papers  (2024-03-21T05:57:27Z)
• ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent United Neural Networks [18.66289473659838]
  We propose an innovative deep learning approach for imputing missing data. A graph attention architecture is employed to capture the spatial correlations present in traffic data. A bidirectional neural network is utilized to learn temporal information.
  arXiv  Detail & Related papers  (2023-05-10T22:15:40Z)
• Demystifying Map Space Exploration for NPUs [4.817475305740601]
  Map Space Exploration is the problem of finding optimized mappings of a Deep Neural Network (DNN) model. We do a first-of-its-kind apples-to-apples comparison of search techniques leveraged by different mappers. Next, we propose two new techniques that can augment existing mappers.
  arXiv  Detail & Related papers  (2022-10-07T17:58:45Z)
• TraClets: Harnessing the power of computer vision for trajectory classification [0.9405458160620532]
  This research is presented that exploits image representations of trajectories, called TraClets, in order to classify trajectories in an intuitive humans way. Several real-world datasets are used to evaluate the proposed approach and compare its classification performance to other state-of-the-art trajectory classification algorithms.
  arXiv  Detail & Related papers  (2022-05-27T10:28:05Z)
• Weakly Supervised Change Detection Using Guided Anisotropic Difusion [97.43170678509478]
  We propose original ideas that help us to leverage such datasets in the context of change detection. First, we propose the guided anisotropic diffusion (GAD) algorithm, which improves semantic segmentation results. We then show its potential in two weakly-supervised learning strategies tailored for change detection.
  arXiv  Detail & Related papers  (2021-12-31T10:03:47Z)
• Deep Learning and Traffic Classification: Lessons learned from a commercial-grade dataset with hundreds of encrypted and zero-day applications [72.02908263225919]
  We share our experience on a commercial-grade DL traffic classification engine. We identify known applications from encrypted traffic, as well as unknown zero-day applications. We propose a novel technique, tailored for DL models, that is significantly more accurate and light-weight than the state of the art.
  arXiv  Detail & Related papers  (2021-04-07T15:21:22Z)
• Geo-Spatiotemporal Features and Shape-Based Prior Knowledge for Fine-grained Imbalanced Data Classification [63.916371837696396]
  Fine-grained classification aims at distinguishing between items with similar global perception and patterns, but that differ by minute details. Our primary challenges come from both small inter-class variations and large intra-class variations. We propose to combine several innovations to improve fine-grained classification within the use-case of wildlife.
  arXiv  Detail & Related papers  (2021-03-21T02:01:38Z)
• ReliefE: Feature Ranking in High-dimensional Spaces via Manifold Embeddings [0.0]
  Relief family of algorithms assign importances to features by iteratively accounting for nearest relevant and irrelevant instances. Recent embedding-based methods learn compact, low-dimensional representations. ReliefE algorithm is faster and can result in better feature rankings.
  arXiv  Detail & Related papers  (2021-01-23T20:23:31Z)
• AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning [72.99415402575886]
  Outlier detection is an important data mining task with numerous practical applications. We propose AutoOD, an automated outlier detection framework, which aims to search for an optimal neural network model. Experimental results on various real-world benchmark datasets demonstrate that the deep model identified by AutoOD achieves the best performance.
  arXiv  Detail & Related papers  (2020-06-19T18:57:51Z)
• Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference [49.88536971774444]
  Inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots. Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet-of-Things applications and services. We present and release the Oxford Inertial Odometry dataset (OxIOD), a first-of-its-kind public dataset for deep learning based inertial navigation research.
  arXiv  Detail & Related papers  (2020-01-13T04:41:54Z)

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.