Results 21 to 30 of about 4,416,987 (284)
Boosting Independent Component Analysis
IEEE Signal Processing Letters, 2022 Independent component analysis is intended to recover the mutually independent components from their linear mixtures. This technique has been widely used in many fields, such as data analysis, signal processing, and machine learning. To alleviate the dependency on prior knowledge concerning unknown sources, many nonparametric methods have been proposed.
YunPeng Li 0003, Zhaohui Ye
openaire +2 more sources
Scatter Matrices and Independent Component Analysis
Austrian Journal of Statistics, 2016 In the independent component analysis (ICA) it is assumed that the components of the multivariate independent and identically distributed observations are linear transformations of latent independent components.
Hannu Oja, Seija Sirkiä, Jan Eriksson
doaj +1 more source
Fourth Moments and Independent Component Analysis [PDF]
, 2015 In independent component analysis it is assumed that the components of the observed random vector are linear combinations of latent independent random variables, and the aim is then to find an estimate for a transformation matrix back to these ...
Miettinen, Jari +3 more
core +1 more source
Egyptian Journal of Remote Sensing and Space Sciences, 2021 The main objective of this research is to evaluate change detection techniques to monitoring land-cover changes that occurred between 2016 and 2017 in the study area located in new administrative capital region in Cairo Governorate, Egypt. The Study area
Ahmed Saber +3 more
doaj +1 more source
Shifted Independent Component Analysis [PDF]
, 2007 Delayed mixing is a problem of theoretical interest and practical importance, e.g., in speech processing, bio-medical signal analysis and financial data modelling. Most previous analyses have been based on models with integer shifts, i.e., shifts by a number of samples, and have often been carried out using time-domain representation.
Morten Mørup +2 more
openaire +1 more source
Multiview Independent Component Analysis with Delays
2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP), 2023 Linear Independent Component Analysis (ICA) is a blind source separation technique that has been used in various domains to identify independent latent sources from observed signals. In order to obtain a higher signal-to-noise ratio, the presence of multiple views of the same sources can be used. In this work, we present MultiView Independent Component
Heurtebise, Ambroise +2 more
openaire +3 more sources
Notion of information and independent component analysis [PDF]
Applications of Mathematics, 2020 Partial orderings and measures of information for continuous univariate random variables with special roles of Gaussian and uniform distributions are discussed. The information measures and measures of non-Gaussianity including third and fourth cumulants are generally used as projection indices in the projection pursuit approach for the independent ...
Radojičić, Una +2 more
openaire +3 more sources
Theta but not beta power is positively associated with better explicit motor task learning
NeuroImage, 2021 Neurophysiologic correlates of motor learning that can be monitored during neurorehabilitation interventions can facilitate the development of more effective learning methods.
Joris van der Cruijsen +7 more
doaj +1 more source
Quantifying identifiability in independent component analysis [PDF]
, 2014 We are interested in consistent estimation of the mixing matrix in the ICA model, when the error distribution is close to (but different from) Gaussian.
Falkeborg, Benjamin +2 more
core +2 more sources
Applied Sciences, 2021 Brain signals can be measured using multiple imaging modalities, such as magnetic resonance imaging (MRI)-based techniques. Different modalities convey distinct yet complementary information; thus, their joint analyses can provide valuable insight into ...
Chunying Jia +5 more
doaj +1 more source