Multi-view outlier detection algorithm based on tensor representation

An outlier detection, multi-view technology, applied in computing, computer parts, instruments, etc., can solve problems such as insufficient use of interactive information and high complexity
CN112116033APending Publication Date: 2020-12-22NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
Publication Date
2020-12-22

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Abstract

The tensor is capable of sufficiently capturing possible relationships between a plurality of views of data when representing multi-view data, while also avoiding pairwise comparison between views. According to we know, a multi-view outlier detection mode based on tensor representation has not been studied up to now. Existing multi-view outlier detection methods mostly employ cross-view pairwise constraints to obtain new feature representations and define outlier scoring metrics according to the features, which do not make full use of interaction information between views and result in highercomplexity in facing three or more views. In order to make up the defect, the invention provides a new multi-view outlier detection algorithm based on tensor representation. Specifically, firstly, multi-view data is reshaped into a tensor set form, then low-rank representation of the tensor set form is learned, and finally an outlier function under tensor representation is designed to achieve detection.
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Description

technical field

[0001] The invention belongs to the field of machine learning, and in particular relates to a multi-view outlier detection method based on tensor representation, which is used to solve the problem of outlier detection in a multi-view scene. Background technique

[0002] Outlier detection, also known as anomaly detection, is a data analysis technique used to identify unusual samples in a dataset. In recent years, a large number of outlier detection methods have been developed. However, these outlier detection algorithms are designed for single-view data and are not suitable for multi-view outlier detection scenarios.

[0003] In reality, many data usually come from different domains or different feature extractors, and each set of features can be regarded as a specific view, thus forming multi-view data. Since feature extraction is often disturbed by noise, outliers are often prone to appear in multi-view data, which will affect subsequent tasks. Therefore, ...

Claims

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