Probabilistic latent semantic analysis for multichannel biomedical signal clustering

Journal article


Wang, Jin and She, Mary. (2016). Probabilistic latent semantic analysis for multichannel biomedical signal clustering. IEEE Signal Processing Letters. 23(12), pp. 1821 - 1824. https://doi.org/10.1109/LSP.2016.2623801
AuthorsWang, Jin and She, Mary
Abstract

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.

Keywordsbiological system modeling; dictionaries; time series analysis; discrete wavelet transforms; robustness; clustering methods; clustering algorithms
Year2016
JournalIEEE Signal Processing Letters
Journal citation23 (12), pp. 1821 - 1824
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISSN1070-9908
Digital Object Identifier (DOI)https://doi.org/10.1109/LSP.2016.2623801
Scopus EID2-s2.0-85011949861
Page range1821 - 1824
Research GroupInstitute for Learning Sciences and Teacher Education (ILSTE)
Publisher's version
File Access Level
Controlled
Place of publicationUnited States
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