Wang, J. & She, M. (2016). Probabilistic latent semantic analysis for multichannel biomedical signal clustering. IEEE Signal Processing Letters,23(12), 1821-1824. United States: Institute of Electrical and Electronics Engineers Inc.. Retrieved from https://doi.org/10.1109/LSP.2016.2623801
This letter extends probabilistic latent semantic analysis (pLSA) for multichannel biomedical signal clustering. The proposed multichannel pLSA (M-pLSA) models a multichannel signal as a generative process of local segments. It directly represents a biomedical signal as a mixture of latent topics based on the assumption that local segments extracted from each channel are conditionally independent given the topics. The categories of biomedical signals are automatically discovered in an unsupervised way. Experimental results demonstrate that the proposed M-pLSA model outperforms previous state-of-the-art methods and is robust to noise contamination.
Learning Sciences Institute Australia
Access may be restricted.