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A new fixed-point algorithm for independent component analysis
2004
Neurocomputing
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A new ÿxed-point algorithm for independent component analysis (ICA) is presented that is able blindly to separate mixed signals with sub-and super-Gaussian source distributions. ...
The new ÿxed-point algorithm maximizes the likelihood very fast and reliably. The validity of this algorithm is conÿrmed by the simulations and experimental results. ...
The authors would like to thank the reviewers for their useful comments and suggestions. ...
doi:10.1016/j.neucom.2003.09.002
fatcat:4hd5ud5ikfgz3k7f553dhrbazy
Effect of Finite Register Length on Bacterial Foraging Optimization based ICA and Constrained Genetic Algorithm based ICA Algorithm
2008
2008 International Conference on Signal Processing, Communications and Networking
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Independent Component Analysis (ICA) specifications [11]. ...
Both CGAICA Genetic Algorithm is used for IC estimation in a constrained manner. ...
the essential before implementation Independent Component Analysis estimates both of a system [10] . ...
doi:10.1109/icscn.2008.4447197
fatcat:tbs4pkwsinearlplmhblrsc2ge
Fast and robust fixed-point algorithms for independent component analysis
1999
IEEE Transactions on Neural Networks
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Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible ...
Finally, we introduce simple fixed-point algorithms for practical optimization of the contrast functions. These algorithms optimize the contrast functions very fast and reliably. ...
Fast and Robust Fixed-Point Algorithms for Independent Component Analysis Aapo Hyvärinen Abstract-Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional ...
doi:10.1109/72.761722
pmid:18252563
fatcat:5jngho43xfhs3jcs4st7cogx7q
Implementation of Optimized Floating Point Ndependent Component Analysis Processor on FPGA for EEG Separation
2012
Journal of Signal Processing Theory and Applications
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Independent component analysis (ICA) is a statistical signal processing technique for separating mixed voices, images and signal. ...
The basic idea of ICA is to find the underlying independent components in the mixture by searching for a linear or nonlinear transformation and minimizing the statistical dependence between components. ...
Oja (1997) developed a fast fixed-point algorithm for independent component analysis. ...
doi:10.7726/jspta.2012.1004
fatcat:qhs5i6kyvnhmjmg3z5f42744zy
A new constrained fixed-point algorithm for ordering independent components
2008
Journal of Computational and Applied Mathematics
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Next, we incorporate the new fixed-point (newFP) algorithm into this constrained ICA model to construct a new constrained fixed-point algorithm. ...
Independent component analysis (ICA) aims to recover a set of unknown mutually independent components (ICs) from their observed mixtures without knowledge of the mixing coefficients. ...
The authors would like to thank the referees and the editorial board for their insightful comments and suggestions. ...
doi:10.1016/j.cam.2007.09.010
fatcat:twkrtwxkarembhvafkckktwaze
Algorithms for Independent Components Analysis and Higher Order Statistics
1999
Neural Information Processing Systems
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A latent variable generative model with finite noise is used to describe several different algorithms for Independent Components Analysis (lCA). ...
In particular, the Fixed Point ICA algorithm is shown to be equivalent to the Expectation-Maximization algorithm for maximum likelihood under certain constraints, allowing the conditions for global convergence ...
We also thank Hagai Attias, Simon Haykin , Juha Karhunen, Te-Won Lee, Erkki Oja, Sebastian Seung, Boris Shraiman, and Oren Shriki for helpful discussions. ...
dblp:conf/nips/LeeRS99
fatcat:4t2bynrc7jd2lojrjnz6ld4qxy
A Novel Floating Point Fast Confluence Adaptive Independent Component Analysis for Signal Processing Applications
2013
Advances in Signal Processing
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Fixed point ICA algorithms cover only smaller range of numbers. ...
Independent component analysis (ICA) is a technique that separates the independent source signals from their mixtures by minimizing the statistical dependence between components. ...
Fixed-Point Iteration for Finding Several ICs More than one independent components are estimated 40 A Novel Floating Point Fast Confluence Adaptive Independent Component Analysis for Signal Processing ...
doi:10.13189/asp.2013.010301
fatcat:bvcsxtadfrfzbh6y5pim47epum
Blind Image Separation Based on an Optimized Fast Fixed Point Algorithm
2013
Advanced Materials Research
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An optimized fast fixed point algorithm based on modified Newton iteration method has been proposed. ...
With good performance ofthe blind image separation, the optimized algorithm can improve the convergence speed greatly.We proposed a new adaptive enhancement parameter to enhance the separated images effectively ...
It's based on a fixed point iteration scheme for finding a maximum of the nongaussianity of ICs. The algorithm could be derived by using the classic Newton iteration method (CN) [10] . ...
doi:10.4028/www.scientific.net/amr.756-759.3578
fatcat:fckopk2qfrg27fiywhhrbaw6ve
Comparison study of fast independent component analysis and constrained independent component analysis
2018
Vibroengineering PROCEDIA
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Constrained Independent Component Analysis (cICA) was developed from Independent Component Analysis (ICA), and the concepts and principles of independent component analysis still apply to constrained independent ...
Finally, the shortcomings of fast independent component analysis and the advantages of constrained independent component analysis are analyzed. ...
. 2) Introduction of constrained independent component analysis algorithm. ...
doi:10.21595/vp.2018.20089
fatcat:vrs4zrr76jhwlgs43sxofanxfq
Performance of Various ICA Algorithms for an Electrocardiogram Signal
2019
International journal of recent technology and engineering
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This work relates the performance of three of the Independent Component Analysis Algorithms such as JADE (Joint Approximate Diagonalisation of Eigen Matrices), Fixed Point ICA (Fast ICA) and AMUSE (Algorithm ...
A suitable technique to overcome these problems is the appropriate use of Independent Component Analysis to maximize the required statistical parameters to make the output to be correlated. ...
fixed-point algorithm). ...
doi:10.35940/ijrte.d7390.118419
fatcat:iq25pf2arbhyvagey7rbwdh5hm
Independent Component Analysis Based on Learning Updating with Forms of Matrix Transformations and the Diagonalization Principle
2006
2006 Japan-China Joint Workshop on Frontier of Computer Science and Technology
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This paper presents a new type of algorithm for solving independent component analysis (ICA) problems. ...
We also analyze the relationship between the new algorithm with other well-known algorithms, such as the Bussgang algorithm, the non-linear principal component analysis (PCA), and the Fas-tICA. ...
This paper presents a new type of algorithm for solving independent component analysis (ICA) problems. ...
doi:10.1109/fcst.2006.16
dblp:conf/fcst/Ding06
fatcat:od5zys7epnbpri4n7fqjn2r2iu
Spatial independent component analysis of functional MRI time-series: To what extent do results depend on the algorithm used?
2002
Human Brain Mapping
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Independent component analysis (ICA) has been successfully employed to decompose functional MRI (fMRI) time-series into sets of activation maps and associated time-courses. ...
We compared the two ICA algorithms that have been used so far for spatial ICA (sICA) of fMRI time-series: the Infomax (Bell and Sejnowski [1995] : Neural Comput 7:1004-1034) and the Fixed-Point (Hyva ¨ ...
for the residual noise in the Infomax generated CTR component with respect to the Fixed-Point approach. ...
doi:10.1002/hbm.10034
pmid:12112768
fatcat:2km3kqsa7ralnc5p3xr5uw7oja
Improved Fast ICA Algorithm Using Eighth-Order Newton's Method
2013
Research Journal of Applied Sciences Engineering and Technology
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Independent Component Analysis (ICA) is a computational method to solve Blind Source Separation (BSS) problem. ...
Eight-order Newton's method for finding the solution of nonlinear equations is much faster than ordinary Newton's iterative method. ...
INDEPENDENT COMPONENT ANALYSIS AND FASTICA Independent Component Analysis (ICA) has become a powerful method for signal processing in last decade (Cichochi and Amari, 2004; Hevarinen et al., 2001; Chio ...
doi:10.19026/rjaset.6.3905
fatcat:q65yqhn7ijgelngwp4n6knug2i
Semi-supervised anomaly detection – towards model-independent searches of new physics
2012
Journal of Physics, Conference Series
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To complement such model-dependent searches, we propose an algorithm based on semi-supervised anomaly detection techniques, which does not require a MC training sample for the signal data. ...
We then search for deviations from this model by fitting to the observations a mixture of the background model and a number of additional Gaussians. ...
Conclusions We presented a novel and self-consistent framework for model-independent searches of new physics based on a semi-supervised anomaly detection algorithm. ...
doi:10.1088/1742-6596/368/1/012032
fatcat:ywjccojnlreq7azovtypbt6uma
One-unit Learning Rules for Independent Component Analysis
1996
Neural Information Processing Systems
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In these new algorithms, every ICA neuron develops into a separator that finds one of the independent components. ...
Neural one-unit learning rules for the problem of Independent Component Analysis (ICA) and blind source separation are introduced. ...
This problem can be represented in a way similar to eq. ( 1 ), replacing the matrix A by a filter. The current neural algorithms for Independent Component Analysis, e.g. ...
dblp:conf/nips/HyvarinenO96
fatcat:wtltnuvrynh6rbtxg3dnn7zjt4
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