Unsupervised source separation for multi-label classification

Imene Mitiche, Alireza Salimy, Philip Boreham, Tony McGrail, Alan Nesbitt, Gordon Morison

Research output: Chapter in Book/Report/Conference proceedingConference contribution


This paper exploits blind source separation for the purpose of multi-label classification (MLC). The proposed method addresses the multi-label classification problem in two stages, source separation of data consisting of two or more observed classes followed by a single-label classification. This method is evaluated on two data-sets: the new application to electromagnetic interference (EMI) signals that are used for high-voltage power asset condition monitoring, and overlapping handwritten digits (multi-MNIST) for further model validation. Our approach is compared to other deep neural networks such as variational auto-encoder (VAE) and capsule network (CapsNet). Results show high average precision on both data-sets and outperform in most metrics other deep auto-encoding networks
that infer label decision from the latent variables distribution.
Original languageEnglish
Title of host publicationProceedings of the 30th European Signal Processing Conference
Number of pages5
Publication statusAccepted/In press - 16 May 2022


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