Tensor-based anomaly detection
Hadi Fanaee-T,João Gama +1 more
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TLDR
The interdisciplinary works in which TAD is reported are surveyed and characterized to characterize the learning strategies, methods and applications; extract the important open issues in TAD and provide the corresponding existing solutions according to the state-of-the-art.Abstract:
Traditional spectral-based methods such as PCA are popular for anomaly detection in a variety of problems and domains. However, if data includes tensor (multiway) structure (e.g. space-time-measurements), some meaningful anomalies may remain invisible with these methods. Although tensor-based anomaly detection (TAD) has been applied within a variety of disciplines over the last twenty years, it is not yet recognized as a formal category in anomaly detection. This survey aims to highlight the potential of tensor-based techniques as a novel approach for detection and identification of abnormalities and failures. We survey the interdisciplinary works in which TAD is reported and characterize the learning strategies, methods and applications; extract the important open issues in TAD and provide the corresponding existing solutions according to the state-of-the-art.read more
Citations
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Journal ArticleDOI
Low-Rank Tensor Networks for Dimensionality Reduction and Large-Scale Optimization Problems: Perspectives and Challenges PART 1.
TL;DR: In this paper, the authors provide mathematical and graphical representations and interpretation of tensor networks, with the main focus on the Tucker and Tensor Train (TT) decompositions and their extensions or generalizations.
Book
Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions
TL;DR: In this paper, the authors provide innovativesolutions to low-rank tensor network decompositions and easy to interpretgraphical representations of the mathematical operations ontensor networks, and demonstrate the ability of tensor networks to provide linearly or even super-linearly e.g., logarithmically scalablesolutions, as illustrated in detail in Part 2.
Journal ArticleDOI
Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data
TL;DR: A systematic review of various state-of-the-art data preprocessing tricks as well as robust principal component analysis methods for process understanding and monitoring applications and big data perspectives on potential challenges and opportunities have been highlighted.
Journal ArticleDOI
Voila: Visual Anomaly Detection and Monitoring with Streaming Spatiotemporal Data
TL;DR: This work proposes a novel tensor-based anomaly analysis algorithm with visualization and interaction design that dynamically produces contextualized, interpretable data summaries and allows for interactively ranking anomalous patterns based on user input.
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K-Means-based isolation forest
TL;DR: In this article, the k-Means-Based Isolation Forest (k-means-based Isolation forest) is proposed for anomaly detection in data, which allows to build a search tree based on many branches in contrast to the only two considered in the original method.
References
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Anomaly detection: A survey
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Journal ArticleDOI
Tensor Decompositions and Applications
Tamara G. Kolda,Brett W. Bader +1 more
TL;DR: This survey provides an overview of higher-order tensor decompositions, their applications, and available software.