Multiple Hypothesis Testing Framework for Spatial Signals

• URL: http://arxiv.org/abs/2108.12314v1
• Date: Fri, 27 Aug 2021 14:48:51 GMT
• Title: Multiple Hypothesis Testing Framework for Spatial Signals
• Authors: Martin G\"olz and Abdelhak M. Zoubir and Visa Koivunen
• Abstract summary: We develop a general framework stemming from multiple hypothesis testing to identify such regions. The spatial grid points associated with different hypotheses are identified while controlling the false discovery rate at a pre-specified level. We propose a novel, data-driven method to estimate local false discovery rates based on the spectral method of moments.
• Score: 42.95566109115774
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The problem of identifying regions of spatially interesting, different or adversarial behavior is inherent to many practical applications involving distributed multisensor systems. In this work, we develop a general framework stemming from multiple hypothesis testing to identify such regions. A discrete spatial grid is assumed for the monitored environment. The spatial grid points associated with different hypotheses are identified while controlling the false discovery rate at a pre-specified level. Measurements are acquired using a large-scale sensor network. We propose a novel, data-driven method to estimate local false discovery rates based on the spectral method of moments. Our method is agnostic to specific spatial propagation models of the underlying physical phenomenon. It relies on a broadly applicable density model for local summary statistics. In between sensors, locations are assigned to regions associated with different hypotheses based on interpolated local false discovery rates. The benefits of our method are illustrated by applications to spatially propagating radio waves.

Related papers

• Score-Based Change-Point Detection and Region Localization for Spatio-Temporal Point Processes [14.068679218846894]
  We propose a likelihood-free, score-based detection framework that jointly estimates the change time and the change region in continuous space-time.<n> operating sequentially the procedure both outputs a stopping time and an estimated change region, enabling real-time detection with spatial interpretability.
  arXiv  Detail & Related papers  (2026-02-04T17:44:41Z)
• Isolation-based Spherical Ensemble Representations for Anomaly Detection [60.989157958972356]
  Anomaly detection is a critical task in data mining and management with applications spanning fraud detection, network security, and log monitoring.<n>Existing unsupervised anomaly detection methods face fundamental challenges including conflicting distributional assumptions, computational inefficiency, and difficulty handling different anomaly types.<n>We propose ISER (Isolation-based Spherical Ensemble Representations) that extends existing isolation-based methods by using hypersphere radii as proxies for local density characteristics while maintaining linear time and constant space complexity.
  arXiv  Detail & Related papers  (2025-10-15T09:00:05Z)
• Spatially-Delineated Domain-Adapted AI Classification: An Application for Oncology Data [3.0566763412020714]
  Given multi-type point maps from different place-types, our objective is to develop a trained on the source place-type to accurately distinguish between two classes of the target place-type based on their point arrangements. This problem is societally important for many applications, such as generating clinical hypotheses for designing new immunotherapies for cancer treatment. We explore a novel multi-task self-learning framework that targets spatial arrangements, such as spatial mix-up masking and spatial contrastive predictive coding, for spatially-delineated AI classification.
  arXiv  Detail & Related papers  (2025-01-20T19:20:13Z)
• DiffuBox: Refining 3D Object Detection with Point Diffusion [74.01759893280774]
  We introduce a novel diffusion-based box refinement approach to ensure robust 3D object detection and localization. We evaluate this approach under various domain adaptation settings, and our results reveal significant improvements across different datasets.
  arXiv  Detail & Related papers  (2024-05-25T03:14:55Z)
• Embedding Trajectory for Out-of-Distribution Detection in Mathematical Reasoning [50.84938730450622]
  We propose a trajectory-based method TV score, which uses trajectory volatility for OOD detection in mathematical reasoning. Our method outperforms all traditional algorithms on GLMs under mathematical reasoning scenarios. Our method can be extended to more applications with high-density features in output spaces, such as multiple-choice questions.
  arXiv  Detail & Related papers  (2024-05-22T22:22:25Z)
• Zone Evaluation: Revealing Spatial Bias in Object Detection [69.59295428233844]
  A fundamental limitation of object detectors is that they suffer from "spatial bias" We present a new zone evaluation protocol, which measures the detection performance over zones. For the first time, we provide numerical results, showing that the object detectors perform quite unevenly across the zones.
  arXiv  Detail & Related papers  (2023-10-20T01:44:49Z)
• WiFi Based Distance Estimation Using Supervised Machine Learning [0.0]
  In recent years WiFi became the primary source of information to locate a person or device indoor. measuring the spatial distance between given set of WiFi fingerprints is heavily affected by the selection of the signal distance function. In this study, the authors proposed utilization of machine learning to improve the estimation of geospatial distance between fingerprints.
  arXiv  Detail & Related papers  (2022-08-15T13:48:46Z)
• Robust Calibration with Multi-domain Temperature Scaling [86.07299013396059]
  We develop a systematic calibration model to handle distribution shifts by leveraging data from multiple domains. Our proposed method -- multi-domain temperature scaling -- uses the robustness in the domains to improve calibration under distribution shift.
  arXiv  Detail & Related papers  (2022-06-06T17:32:12Z)
• Learning-based Localizability Estimation for Robust LiDAR Localization [13.298113481670038]
  LiDAR-based localization and mapping is one of the core components in many modern robotic systems. This work proposes a neural network-based estimation approach for detecting (non-)localizability during robot operation.
  arXiv  Detail & Related papers  (2022-03-11T01:12:00Z)
• Local Relation Learning for Face Forgery Detection [73.73130683091154]
  We propose a novel perspective of face forgery detection via local relation learning. Specifically, we propose a Multi-scale Patch Similarity Module (MPSM), which measures the similarity between features of local regions. We also propose an RGB-Frequency Attention Module (RFAM) to fuse information in both RGB and frequency domains for more comprehensive local feature representation.
  arXiv  Detail & Related papers  (2021-05-06T10:44:32Z)
• Uncertainty Aware Deep Neural Network for Multistatic Localization with Application to Ultrasonic Structural Health Monitoring [0.0]
  This paper uses an uncertainty-aware deep neural distribution network framework to learn robust localization models. We show that the predictive uncertainty scales as environmental uncertainty increases to provide a statistically meaningful metric for assessing localization accuracy.
  arXiv  Detail & Related papers  (2020-07-14T04:53:06Z)
• On Cokriging, Neural Networks, and Spatial Blind Source Separation for Multivariate Spatial Prediction [3.416170716497814]
  Blind source separation is a pre-processing tool for spatial prediction. In this paper we investigate the use of spatial blind source separation as a pre-processing tool for spatial prediction. We compare it with predictions from Cokriging and neural networks in an extensive simulation study as well as a geochemical dataset.
  arXiv  Detail & Related papers  (2020-07-01T10:59:45Z)
• Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation [62.29076080124199]
  This paper proposes a novel coarse-to-fine feature adaptation approach to cross-domain object detection. At the coarse-grained stage, foreground regions are extracted by adopting the attention mechanism, and aligned according to their marginal distributions. At the fine-grained stage, we conduct conditional distribution alignment of foregrounds by minimizing the distance of global prototypes with the same category but from different domains.
  arXiv  Detail & Related papers  (2020-03-23T13:40:06Z)
• A flexible outlier detector based on a topology given by graph communities [0.0]
  anomaly detection is essential for optimal performance of machine learning methods and statistical predictive models. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. Our approach overall outperforms, both, local and global strategies in multi and single view settings.
  arXiv  Detail & Related papers  (2020-02-18T18:40:31Z)

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.