A Review of Independent Component Analysis Techniques and Their Applications

Abstract Independent Component Analysis, a computationally efficient blind statistical signal processing technique, has been an area of interest for researchers for many practical applications in various fields of science and engineering. The present paper attempts to treat the fundamental concepts involved in the independent component analysis (ICA) technique and reviews different ICA algorithms. A thorough discussion of the algorithms with their merits and weaknesses has been carried out. Applications of the ICA algorithms in different fields of science and technology have been reviewed. The limitations and ambiguities of the ICA techniques developed so far have also been outlined. Though several articles have reviewed the ICA techniques in literature, they suffer from the limitation of not being comprehensive to a first time reader or not incorporating the latest available algorithm and their applications. In this work, we present different ICA algorithms from their basics to their potential applications to serve as a comprehensive single source for an inquisitive researcher to carry out his work in this field.

[1]  Andrzej Cichocki,et al.  Bias removal technique for blind source separation with noisy measurements , 1998 .

[2]  J. Cardoso,et al.  Blind beamforming for non-gaussian signals , 1993 .

[3]  Erkki Oja,et al.  Independent component analysis by general nonlinear Hebbian-like learning rules , 1998, Signal Process..

[4]  Dean J. Krusienski,et al.  Nonparametric density estimation based independent component analysis via particle swarm optimization , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[5]  Asoke K. Nandi,et al.  Maximisation of squared cumulants for blind source separation , 2000 .

[6]  I. Johnstone,et al.  Wavelet Shrinkage: Asymptopia? , 1995 .

[7]  Yingwei Zhang,et al.  Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM , 2009 .

[8]  Jordi Vitrià,et al.  Bayesian Classification of Cork Stoppers Using Class-Conditional Independent Component Analysis , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[9]  Marc Moonen,et al.  Improving Auditory Steady-State Response Detection Using Independent Component Analysis on Multichannel EEG Data , 2007, IEEE Transactions on Biomedical Engineering.

[10]  Keinosuke Fukunaga,et al.  An Algorithm for Finding Intrinsic Dimensionality of Data , 1971, IEEE Transactions on Computers.

[11]  Jun Wang,et al.  Nonlinear Blind Source Separation Using Higher Order Statistics and a Genetic Algorithm , 2001 .

[12]  Andrew D. Back,et al.  A First Application of Independent Component Analysis to Extracting Structure from Stock Returns , 1997, Int. J. Neural Syst..

[13]  Nathalie Delfosse,et al.  Adaptive blind separation of independent sources: A deflation approach , 1995, Signal Process..

[14]  Issam Dagher,et al.  Face recognition using IPCA-ICA algorithm , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[16]  G. Capizzi,et al.  A New Tool for the Identification and Localization of Electromagnetic Sources by Using Independent Component Analysis , 2006, IEEE Transactions on Magnetics.

[17]  E. Oja,et al.  Convergence of the symmetrical FastICA algorithm , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[18]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

[19]  Christian Jutten,et al.  Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..

[20]  Nikos Fakotakis,et al.  Independent component analysis applied to feature extraction for robust automatic speech recognition , 2000 .

[21]  Andrzej Cichocki,et al.  A robust approach to independent component analysis of signals with high-level noise measurements , 2003, IEEE Trans. Neural Networks.

[22]  Xiaohui Zhao,et al.  Linear System Identification Employing Independent Component Analysis , 2007, 2007 IEEE International Conference on Automation and Logistics.

[23]  Cedric Nishan Canagarajah,et al.  Improving Fusion of Surveillance Images in Sensor Networks Using Independent Component Analysis , 2007, IEEE Transactions on Consumer Electronics.

[24]  E. Oja,et al.  Independent Component Analysis , 2013 .

[25]  Asoke K. Nandi,et al.  Fourth-order cumulant based blind source separation , 1996, IEEE Signal Processing Letters.

[26]  S. Joe Qin,et al.  Fault Detection of Nonlinear Processes Using Multiway Kernel Independent Component Analysis , 2007 .

[27]  R. Palaniappan,et al.  Genetic algorithm based independent component analysis to separate noise from Electrocardiogram signals , 2006, 2006 IEEE International Conference on Engineering of Intelligent Systems.

[28]  Andrzej Cichocki,et al.  Robust neural networks with on-line learning for blind identification and blind separation of sources , 1996 .

[29]  Bernhard Schölkopf,et al.  Real-Time Fetal Heart Monitoring in Biomagnetic Measurements Using Adaptive Real-Time ICA , 2007, IEEE Transactions on Biomedical Engineering.

[30]  Aapo Hyvärinen,et al.  A Fast Fixed-Point Algorithm for Independent Component Analysis , 1997, Neural Computation.

[31]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[32]  E.E. Pissaloux,et al.  Image Processing , 1994, Proceedings. Second Euromicro Workshop on Parallel and Distributed Processing.

[33]  S. Kawasaki,et al.  Springer Verlag, Berlin, Heidelberg, New York (1995) , 1996 .

[34]  Bogdan Matei,et al.  A Review of Independent Component Analysis Techniques , 2022 .

[35]  Asoke K. Nandi,et al.  Fourth-order cumulant based estimator for independent component analysis , 2001 .

[36]  Witold Pedrycz,et al.  Face Recognition Using an Enhanced Independent Component Analysis Approach , 2007, IEEE Transactions on Neural Networks.

[37]  Dinh Tuan Pham,et al.  Separation of a mixture of independent sources through a maximum likelihood approach , 1992 .

[38]  Jean-François Cardoso,et al.  Equivariant adaptive source separation , 1996, IEEE Trans. Signal Process..

[39]  Jean-Francois Cardoso,et al.  Source separation using higher order moments , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[40]  Jean-Francois Cardoso,et al.  Adaptive Source Separation With Uniform Performance , 1994 .

[41]  Aapo Hyvärinen,et al.  A Fast Fixed-Point Algorithm for Independent Component Analysis of Complex Valued Signals , 2000, Int. J. Neural Syst..

[42]  Hideaki Sakai,et al.  A New Adaptive Filter Algorithm for System Identification using Independent Component Analysis , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[43]  T. Ens,et al.  Blind signal separation : statistical principles , 1998 .

[44]  Stan Z. Li,et al.  Learning multiview face subspaces and facial pose estimation using independent component analysis , 2005, IEEE Transactions on Image Processing.

[45]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[46]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[47]  Kazuyoshi Itoh,et al.  An algebraic solution to independent component analysis , 2000 .

[48]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[49]  Erkki Oja,et al.  Independent Component Analysis in Wave Decomposition of Auditory Evoked Fields , 1998 .

[50]  J. Cardoso.  Infomax and maximum likelihood for blind source separation , 1997, IEEE Signal Processing Letters.

[51]  S. Qin,et al.  Improved nonlinear fault detection technique and statistical analysis , 2008 .

[52]  Marian Stewart Bartlett,et al.  Viewpoint Invariant Face Recognition using Independent Component Analysis and Attractor Networks , 1996, NIPS.

[53]  Ignacio Rojas,et al.  Blind source separation in post-nonlinear mixtures using competitive learning, Simulated annealing, and a genetic algorithm , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[54]  Michael I. Jordan,et al.  Kernel independent component analysis , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..