Semi-Supervised Eigenbasis Novelty Detection

被引：0

作者：

Thompson, David R. ^{[1
]}

Majid, Walid A. ^{[1
]}

Reed, Colorado J. ^{[1
]}

Wagstaff, Kiri L. ^{[1
]}

机构：

[1] CALTECH, Jet Prop Lab, Pasadena, CA 91125 USA

来源：

STATISTICAL ANALYSIS AND DATA MINING | 2013年 / 6卷 / 03期

关键词：

novelty detection; time series analysis; radio astronomy; machine learning; anomaly detection; radio transients; fast transients; semi-supervised learning;

D O I：

10.1002/sam.11148

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a semi-supervised online method for novelty detection and evaluate its performance for radio astronomy time series data. Our approach uses sparse, adaptive eigenbases to combine (1) prior knowledge about uninteresting signals with (2) online estimation of the current data properties to enable highly sensitive and precise detection of novel signals. We apply Semi-Supervised Eigenbasis Novelty Detection (SSEND) to the problem of detecting fast transient radio anomalies and compare it to current alternative algorithms. Tests based on observations from the Parkes Multibeam Survey show both effective detection of interesting rare events and robustness to known false alarm anomalies. (c) 2012 Wiley Periodicals, Inc.

引用

页码：195 / 204

页数：10

共 50 条

[21] Poster: Semi-supervised Anomaly Detection on a Tier-0 HPC System
Molan, Martin
Borghesi, Andrea
Benini, Luca
Bartolini, Andrea
PROCEEDINGS OF THE 19TH ACM INTERNATIONAL CONFERENCE ON COMPUTING FRONTIERS 2022 (CF 2022), 2022, : 203 - 204
[22] Semi-Supervised Detection of Collective Anomalies with an Application in High Energy Particle Physics
Vatanen, Tommi
Kuusela, Mikael
Malmi, Eric
Raiko, Tapani
Aaltonen, Timo
Nagai, Yoshikazu
2012 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2012,
[23] Semi-supervised Learning for Generalizable Intracranial Hemorrhage Detection and Segmentation
Lin, Emily
Yuh, Esther L.
RADIOLOGY-ARTIFICIAL INTELLIGENCE, 2024, 6 (03)
[24] A semi-supervised anomaly detection approach for detecting mechanical failures
Soete, Colin
Rademaker, Michael
Van Hoecke, Sofie
AI EDAM-ARTIFICIAL INTELLIGENCE FOR ENGINEERING DESIGN ANALYSIS AND MANUFACTURING, 2024, 38
[25] Semi-supervised Graph Edge Convolutional Network for Anomaly Detection
Lun, Zhicheng
Gu, Xiaoyan
Fan, Haihui
Li, Bo
Wang, Weiping
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2021, PT I, 2021, 12891 : 141 - 152
[26] Case Study: A Semi-Supervised Methodology for Anomaly Detection and Diagnosis
Morales-Forero, A.
Bassetto, S.
2019 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEM), 2019, : 1031 - 1037
[27] ConsE: Consistency Exploitation for Semi-Supervised Anomaly Detection in Graphs
Chang, Wenjing
Yu, Jianjun
Zhou, Xiaojun
2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN, 2023,
[28] AnoOnly: Semi-supervised anomaly detection with the only loss on anomalies
Zhou, Yixuan
Yang, Peiyu
Qu, Yi
Xu, Xing
Sun, Zhe
Cichocki, Andrzej
EXPERT SYSTEMS WITH APPLICATIONS, 2025, 262
[29] Detection of Interictal epileptiform discharges with semi-supervised deep learning
de Sousa, Ana Maria Amaro
van Putten, Michel J. A. M.
van den Berg, Stephanie
Haeri, Maryam Amir
BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2024, 88
[30] Intrusion detection for Softwarized Networks with Semi-supervised Federated Learning
Aouedi, Ons
Piamrat, Kandaraj
Muller, Guillaume
Singh, Kamal
IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2022), 2022, : 5244 - 5249

← 1 2 3 4 5 →