方法证据记录
Independent Component Analysis
Independent Component Analysis (ICA) is a computational method for separating a multivariate signal into additive, statistically independent subcomponents. Formalized by Pierre Comon in 1994, ICA became the foundational framework for blind source separation and is widely applied in neuroimaging (fMRI, EEG), speech processing, and biomedical signal analysis.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Independent Component Analysis (ICA)
分类方法记录 · latent-structure / machine-learning
- Comon, P. (1994). Independent component analysis, a new concept? Signal Processing, 36(3), 287–314. · DOI 10.1016/0165-1684(94)90029-9
- Hyvärinen, A., Karhunen, J., & Oja, E. (2001). Independent Component Analysis. Wiley. · ISBN 978-0-471-40540-5
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