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A new fixed-point algorithm for independent component analysis

Zhenwei Shi, Huanwen Tang, Yiyuan Tang
2004 Neurocomputing  
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

D. P. Acharya, G. Panda, Y. V. S. Lakshmi
2008 2008 International Conference on Signal Processing, Communications and Networking  
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

A. Hyvarinen
1999 IEEE Transactions on Neural Networks  
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

Jayasanthi Ranjith. M.E, NJR. Muniraj
2012 Journal of Signal Processing Theory and Applications  
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

Hongjuan Zhang, Chonghui Guo, Zhenwei Shi, Enmin Feng
2008 Journal of Computational and Applied Mathematics  
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

Daniel D. Lee, Uri Rokni, Haim Sompolinsky
1999 Neural Information Processing Systems  
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

Jayasanthi Ranjith. M.E., Dr.NJR. Muniraj
2013 Advances in Signal Processing  
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

Wei Yuan, Li Yi Zhang
2013 Advanced Materials Research  
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

Shuangxi Jing, Qi Liu, Chenxu Luo, Penghui Shi
2018 Vibroengineering PROCEDIA  
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  
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

Shuxue Ding
2006 2006 Japan-China Joint Workshop on Frontier of Computer Science and Technology  
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?

Fabrizio Esposito, Elia Formisano, Erich Seifritz, Rainer Goebel, Renato Morrone, Gioacchino Tedeschi, Francesco Di Salle
2002 Human Brain Mapping  
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

Tahir Ahmad, Norma Alias, Mahdi Ghanbari, Mohammad Askaripour
2013 Research Journal of Applied Sciences Engineering and Technology  
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

Mikael Kuusela, Tommi Vatanen, Eric Malmi, Tapani Raiko, Timo Aaltonen, Yoshikazu Nagai
2012 Journal of Physics, Conference Series  
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

Aapo Hyvärinen, Erkki Oja
1996 Neural Information Processing Systems  
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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