Cargando…
Independent component analysis: recent advances
Independent component analysis is a probabilistic method for learning a linear transform of a random vector. The goal is to find components that are maximally independent and non-Gaussian (non-normal). Its fundamental difference to classical multi-variate statistical methods is in the assumption of...
Autor principal: | Hyvärinen, Aapo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society Publishing
2013
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3538438/ https://www.ncbi.nlm.nih.gov/pubmed/23277597 http://dx.doi.org/10.1098/rsta.2011.0534 |
Ejemplares similares
-
Nonlinear independent component analysis for principled disentanglement in unsupervised deep learning
por: Hyvärinen, Aapo, et al.
Publicado: (2023) -
Testing Independent Component Patterns by Inter-Subject or Inter-Session Consistency
por: Hyvärinen, Aapo, et al.
Publicado: (2013) -
Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis
por: Hirayama, Jun-ichiro, et al.
Publicado: (2016) -
A mixture of sparse coding models explaining properties of face neurons related to holistic and parts-based processing
por: Hosoya, Haruo, et al.
Publicado: (2017) -
Non-linear canonical correlation for joint analysis of MEG signals from two subjects
por: Campi, Cristina, et al.
Publicado: (2013)