Independent component analysis: an introduction.

By James V. Stone. Trends in Cognitive Sciences, 2002

477 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/8e081a6bead7f9d0088e3508b08b3d81f78ec662

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Tom Eichele
2. Stefan Debener
3. Markus Ullsperger
4. Andreas K. Engel
5. D. von Cramon
6. Vince D. Calhoun
7. Zulfiqur Ali
8. Abdelmalik Taleb-Ahmed

In Signal Processing

1. Tom Eichele
2. Zulfiqur Ali
3. D. von Cramon
4. Abdelmalik Taleb-Ahmed
5. D. Saha
6. Vince D. Calhoun
7. D. James
8. Samira Sadaoui

Citations in the corpus

• Machine learning techniques for analysis of hyperspectral images to determine quality of food products: A review (2021, methodology): “However, in several studies, it has also been reported that the classi fi cation accuracy of the naïve Bayes classi fi er is less than 75%, indicating a weak classi fi cation model (Qin et al., 2020; Siedliska et al., 2017, 2018).”
• Data analysis for electronic nose systems (2006, methodology): “The second are filter methods and work independently to remove features without knowing the effect on the classification algorithm, typical linear methods used are principal component analysis (PCA), linear discriminant analysis (LDA) and independent component analysis (ICA) [36, 37 ].”
• Prediction of human errors by maladaptive changes in event-related brain networks (2008, methodology): “After ICA, we deconvolved HRFs from the timecourses of individual eventrelated components (ICs) to recover individually and regionally specific empirical estimates of the HRF (39, 40) and used these subsequently for estimation of single-trial responses.”
• Past, Present, and Future of Face Recognition: A Review (2020, methodology): “Besides, they implemented two different architectures with the FERET database to test ICA performance; the first process the pictures as random variables and the pixels as results, while the second processed the pixels as random variables, and the pictures as results.”
• Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE) (2021, methodology): “, A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine, Neurocomputing, 55, 1-2, 321-336 (2003) [51] Tharwat, A.”