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
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[0029] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.
[0030] attached figure 1 The overall flowchart of the multi-view outlier detection algorithm based on tensor representation mentioned in the present invention is shown, specifically including the following steps:
[0031] S1: Reshape the original multi-view samples into a tensor representation, thereby forming a multi-view tensor set, and expand each tensor into a vector to obtain the transformed sample matrix.
[0032] S2: Construct the low-rank representation learning objective function of the sample matrix, and calculate the best representation coefficient and error matrix that minimize the objective function value.
[0033] S3: Calculate the outlier scores of all samples according to the representation coefficient and error matrix obtained in step S2, and output the outlier labels of all samples.
[0034] Further, a multi-view outlier de...
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