Semi Blind Source Separation for Application in Machine Learning

Unsupervised learning is a class of problems in machine learning which seeks to determine how the data are organized. Unsupervised learning encompasses many other techniques that seek to summarize and explain key features of the data. One form of unsupervised learning is blind source separation (BSS). BSS is a class of computational data analysis techniques for revealing hidden factors that underlie sets of measurements or signals. BSS assumes a statistical model whereby the observed multivariate data, typically given as a large database of samples, are assumed to be linear or nonlinear mixtures of some unknown latent variables. The mixing coefficients are also unknown. Sometimes more prior information about the sources is available or is induced into the model, such as the form of their probability densities, their spectral contents, etc. Then the term blind is often replaced by semiblind. This chapter reports the semi BSS machine learning applications on audio and bio signal processing. DOI: 10.4018/978-1-4666-1833-6.ch003

[1]  Marimuthu Palaniswami,et al.  Subtle Hand Gesture Identification for HCI Using Temporal Decorrelation Source Separation BSS of Surface EMG , 2007, 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications (DICTA 2007).

[2]  Lionel Tarassenko,et al.  Application of independent component analysis in removing artefacts from the electrocardiogram , 2006, Neural Computing & Applications.

[3]  Kiyohiro Shikano,et al.  Overdetermined blind separation for convolutive mixtures of speech based on multistage ICA using subarray processing , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[4]  Heinz Mathis,et al.  Joint diagonalization of correlation matrices by using gradient methods with application to blind signal separation , 2002, Sensor Array and Multichannel Signal Processing Workshop Proceedings, 2002.

[5]  Carl D. Meyer,et al.  Matrix Analysis and Applied Linear Algebra , 2000 .

[6]  L. K. Hansen,et al.  Independent Components in Text , 2000 .

[7]  Jun Rekimoto,et al.  GestureWrist and GesturePad: unobtrusive wearable interaction devices , 2001, Proceedings Fifth International Symposium on Wearable Computers.

[8]  Antoine Souloumiac,et al.  Jacobi Angles for Simultaneous Diagonalization , 1996, SIAM J. Matrix Anal. Appl..

[9]  Robert B. Randall,et al.  Redundant data elimination in independent component analysis , 2005, Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, 2005..

[10]  Terrence J. Sejnowski,et al.  Blind source separation of more sources than mixtures using overcomplete representations , 1999, IEEE Signal Processing Letters.

[11]  Erkki Oja,et al.  A fast algorithm for estimating overcomplete ICA bases for image windows , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[12]  Michael Zibulevsky,et al.  Underdetermined blind source separation using sparse representations , 2001, Signal Process..

[13]  G.R. Naik,et al.  Performance comparison of ICA algorithms for Isometric Hand gesture identification using Surface EMG , 2007, 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information.

[14]  J.C. Mosher,et al.  Multiple dipole modeling and localization from spatio-temporal MEG data , 1992, IEEE Transactions on Biomedical Engineering.

[15]  Jaime Parra,et al.  Magnetoencephalography: an investigational tool or a routine clinical technique? , 2004, Epilepsy & Behavior.

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

[17]  Terrence J. Sejnowski,et al.  Learning Overcomplete Representations , 2000, Neural Computation.

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

[19]  J.C. Rajapakse,et al.  Independent component analysis and beyond in brain imaging: EEG, MEG, fMRI, and PET , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..

[20]  James V. Stone.  Independent component analysis: an introduction , 2002, Trends in Cognitive Sciences.

[21]  Aude Billard,et al.  Recognition and reproduction of gestures using a probabilistic framework combining PCA, ICA and HMM , 2005, ICML.

[22]  Ole Winther,et al.  Mean-Field Approaches to Independent Component Analysis , 2002, Neural Computation.

[23]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[24]  Russell H. Lambert,et al.  OVERDETERMINED BLIND SOURCE SEPARATION: USING MORE SENSORS THAN SOURCE SIGNALS IN A NOISY MIXTURE , 2000 .

[25]  Barak A. Pearlmutter,et al.  Blind Source Separation by Sparse Decomposition in a Signal Dictionary , 2001, Neural Computation.

[26]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[27]  Petteri Pajunen,et al.  Blind source separation using algorithmic information theory , 1998, Neurocomputing.

[28]  Barak A. Pearlmutter,et al.  Independent Components of Magnetoencephalography: Localization , 2002, Neural Computation.

[29]  Terrence J. Sejnowski,et al.  Unsupervised Classification with Non-Gaussian Mixture Models Using ICA , 1998, NIPS.

[30]  Kiyotoshi Matsuoka,et al.  A neural net for blind separation of nonstationary signals , 1995, Neural Networks.

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

[32]  Marimuthu Palaniswami,et al.  Multi run ICA and surface EMG based signal processing system for recognising hand gestures , 2008, 2008 8th IEEE International Conference on Computer and Information Technology.

[33]  Shun-ichi Amari,et al.  Natural Gradient Learning for Over- and Under-Complete Bases in ICA , 1999, Neural Computation.

[34]  Yen-Wei Chen,et al.  Articulated hand tracking by PCA-ICA approach , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[35]  Lars Kai Hansen,et al.  Blind Separation of Noisy Image Mixtures , 2000 .

[36]  Erkki Oja,et al.  Independent Component Analysis for Identification of Artifacts in Magnetoencephalographic Recordings , 1997, NIPS.

[37]  Erkki Oja,et al.  Independent component approach to the analysis of EEG and MEG recordings , 2000, IEEE Transactions on Biomedical Engineering.

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

[39]  Shiro Ikeda,et al.  A METHOD OF ICA IN TIME-FREQUENCY DOMAIN , 2003 .

[40]  M. Scherg,et al.  Two bilateral sources of the late AEP as identified by a spatio-temporal dipole model. , 1985, Electroencephalography and clinical neurophysiology.