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Independent component analysis of textures [PDF]
Proceedings of the Seventh IEEE International Conference on Computer Vision, 1999 A common method for texture representation is to use the marginal probability densities over the outputs of a set of multi-orientation, multi-scale filters as a description of the texture. We propose a technique, based on independent component analysis, for choosing the set of filters that yield the most informative marginals, meaning that the product ...
Roberto Manduchi, Javier Portilla
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Principal independent component analysis [PDF]
IEEE Transactions on Neural Networks, 1999 Conventional blind signal separation algorithms do not adopt any asymmetric information of the input sources, thus the convergence point of a single output is always unpredictable. However, in most of the applications, we are usually interested in only one or two of the source signals and prior information is almost always available.
Jie Luo 0001 +3 more
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Adaptive Speech Separation Based on Beamforming and Frequency Domain-Independent Component Analysis
Applied Sciences, 2020 Voice signals acquired by a microphone array often include considerable noise and mutual interference, seriously degrading the accuracy and speed of speech separation.
Ke Zhang, Yangjie Wei, Dan Wu, Yi Wang
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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
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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
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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
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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
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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
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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
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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
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