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Dimension reduction method of high-dimensional fault datasets based on C M t-SNE under unsupervised background by Sencai Ma; Gang Cheng; Yong Li; Rongzhen Zhao is a Engineering article available to read on EtoBox.

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The unlabeled fault datasets often contain much non-sensitive redundant, and uncertain information. This study designs a novel interpretable and unsupervised dimension reduction method for unlabeled data containing redundancy and uncertainty. Firstly, a fuzzy-based way for pseudo-label generation is given, and feature cloud models under pseudo labels are established; Secondly, this study takes the expectation, entropy, and hyper entropy of the cloud models representing uncertainty in features as spatial vectors. The difference degree between vectors is treated as the evaluation standard to filter out non-sensitive features based on the maximum initial difference; Moreover, redundant elements are fused by t-SNE, and lower dimensional feature components conducive for fault classification are obtained; Finally, the effectiveness of the method is demonstrated by comparative experiments. The results show that this method has a higher factor, which means that the method can better mine the difference among different faults and improve the performance of fault identification.

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Author

Sencai Ma; Gang Cheng; Yong Li; Rongzhen Zhao

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)