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independent-component-analysis
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A geometric approach for separating several speech signals
, Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; Volume 3195 , 2004 , Pages 798-806 ; 03029743 (ISSN); 3540230564 (ISBN); 9783540230564 (ISBN) ; Mansour, A ; Jutten, C ; Marvasti, F ; Sharif University of Technology
Springer Verlag
2004
Abstract
In this paper a new geometrical approach for separating speech signals is presented. This approach can be directly applied to separate more than two speech signals. It is based on clustering the observation points, and then fitting a line (hyper-plane) onto each cluster. The algorithm quality is shown to be improved by using DCT coefficients of speech signals, as opposed to using speech samples. © Springer-Verlag 2004
Processing polysomnographic signals, using independent component analysis approaches
, Article Proceedings of the IASTED International Conference on Biomedical Engineering, Innsbruck, 16 February 2004 through 18 February 2004 ; 2004 , Pages 193-196 ; 0889863792 (ISBN); 9780889863798 (ISBN) ; Shamsollahi, M. B ; Senhadji, L ; Sharif University of Technology
2004
Abstract
In this paper several applications of the Independent Component Analysis (ICA) algorithm, for the analysis of biomedical signal recordings have been investigated. One of these applications is the removal of EEG artifacts such as the EOG. It is shown that ICA may serve as a powerful tool, which could help the analysis of biomedical recordings, and give better insights about the underlying sources of some disorders. Another application of the proposed method is the detection of sleep disorders in patients suffering from sleep apnea. The ultimate goal of this approach is to develop an automatic noninvasive data acquisition system, for clinical applications
Differential of the Mutual Information
, Article IEEE Signal Processing Letters ; Volume 11, Issue 1 , 2004 , Pages 48-51 ; 10709908 (ISSN) ; Jutten, C ; Nayebi, K ; Sharif University of Technology
2004
Abstract
In this letter, we compute the variation of the mutual information, resulting from a small variation in its argument. Although the result can be applied in many problems, we consider only one example: the result is used for deriving a new method for blind source separation in linear mixtures. The experimental results emphasize the performance of the resulting algorithm
Blind source separation by adaptive estimation of score function difference
, Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; Volume 3195 , 2004 , Pages 9-17 ; 03029743 (ISSN); 3540230564 (ISBN); 9783540230564 (ISBN) ; Babaie Zadeh, M ; Jutten, C ; Nayebi, K ; Sharif University of Technology
Springer Verlag
2004
Abstract
In this paper, an adaptive algorithm for blind source separation in linear instantaneous mixtures is proposed, and it is shown to be the optimum version of the EASI algorithm. The algorithm is based on minimization of mutual information of outputs. This minimization is done using adaptive estimation of a recently proposed non-parametric "gradient" for mutual information. © Springer-Verlag 2004
Using independent component analysis to monitor 2-D geometric specifications
, Article Quality and Reliability Engineering International ; Volume 33, Issue 8 , 2017 , Pages 2075-2087 ; 07488017 (ISSN) ; Niaki, S. T. A ; Noorossana, R ; Sharif University of Technology
Abstract
Functional data and profiles are characterized by complex relationships between a response and several predictor variables. Fortunately, statistical process control methods provide a solid ground for monitoring the stability of these relationships over time. This study focuses on the monitoring of 2-dimensional geometric specifications. Although the existing approaches deploy regression models with spatial autoregressive error terms combined with control charts to monitor the parameters, they are designed based on some idealistic assumptions that can be easily violated in practice. In this paper, the independent component analysis (ICA) is used in combination with a statistical process...
Spatial and temporal joint, partially-joint and individual sources in independent component analysis: Application to social brain fMRI dataset
, Article Journal of Neuroscience Methods ; Volume 329 , 2020 ; Shamsollahi, M. B ; Sharif University of Technology
Elsevier B.V
2020
Abstract
absectionBackground Three types of sources can be considered in the analysis of multi-subject datasets: (i) joint sources which are common among all subjects, (ii) partially-joint sources which are common only among a subset of subjects, and (iii) individual sources which belong to each subject and represent the specific conditions of that subject. Extracting spatial and temporal joint, partially-joint, and individual sources of multi-subject datasets is of significant importance to analyze common and cross information of multiple subjects. New method: We present a new framework to extract these three types of spatial and temporal sources in multi-subject functional magnetic resonance...
Sparse ICA via cluster-wise PCA
, Article Neurocomputing ; Volume 69, Issue 13-15 , 2006 , Pages 1458-1466 ; 09252312 (ISSN) ; Jutten, C ; Mansour, A ; Sharif University of Technology
2006
Abstract
In this paper, it is shown that independent component analysis (ICA) of sparse signals (sparse ICA) can be seen as a cluster-wise principal component analysis (PCA). Consequently, Sparse ICA may be done by a combination of a clustering algorithm and PCA. For the clustering part, we use, in this paper, an algorithm inspired from K-means. The final algorithm is easy to implement for any number of sources. Experimental results points out the good performance of the method, whose the main restriction is to request an exponential growing of the sample number as the number of sources increases. © 2006 Elsevier B.V. All rights reserved
Blind source separation in nonlinear mixtures: separability and a basic algorithm
, Article IEEE Transactions on Signal Processing ; Volume 65, Issue 16 , 2017 , Pages 4339-4352 ; 1053587X (ISSN) ; Babaie Zadeh, M ; Rivet, B ; Jutten, C ; Sharif University of Technology
Abstract
In this paper, a novel approach for performing blind source separation (BSS) in nonlinear mixtures is proposed, and their separability is studied. It is shown that this problem can be solved under a few assumptions, which are satisfied in most practical applications. The main idea can be considered as transforming a time-invariant nonlinear BSS problem to local linear ones varying along the time, using the derivatives of both sources and observations. Taking into account the proposed idea, numerous algorithms can be developed performing the separation. In this regard, an algorithm, supported by simulation results, is also proposed in this paper. It can be seen that the algorithm well...
Malignancy determination of tumors using perfusion MRI
, Article 2009 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2009, Las Vegas, NV, 13 July 2009 through 16 July 2009 ; Volume 2 , 2009 , Pages 906-909 ; 9781601321190 (ISBN) ; Soltanian Zadeh, H ; Akhlaghpour, S ; Fatemi Zadeh, E ; United States Military Academy, Network Science Center; HST Harvard Univ. MIT, Biomed. Cybern. Lab.; Argonne's Leadersh. Comput. Facil. Argonne Natl. Lab.; Univ. Illinois Urbana-Champaign, Funct. Genomics Lab.; University of Minnesota, Minnesota Supercomputing Institute ; Sharif University of Technology
2009
Abstract
Our purpose was to determine whether perfusion MR imaging can be used for malignancy determination of tumors. Relative cerebral blood flow (rCBF) is a commonly used perfusion magnetic resonance imaging (MRI) technique for the evaluation of malignancy. The goal of our study was to determine the usefulness of this parameter in malignancy determination of tumors using Independent Component Analysis (ICA)
Fast sparse representation based on smoothed ℓ0norm
, Article 7th International Conference on Independent Component Analysis (ICA) and Source Separation, ICA 2007, London, 9 September 2007 through 12 September 2007 ; Volume 4666 LNCS , 2007 , Pages 389-396 ; 03029743 (ISSN); 9783540744931 (ISBN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
Springer Verlag
2007
Abstract
In this paper, a new algorithm for Sparse Component Analysis (SCA) or atomic decomposition on over-complete dictionaries is presented. The algorithm is essentially a method for obtaining sufficiently sparse solutions of underdetermined systems of linear equations. The solution obtained by the proposed algorithm is compared with the minimum ℓ1-norm solution achieved by Linear Programming (LP). It is experimentally shown that the proposed algorithm is about two orders of magnitude faster than the state-of-the-art ℓ1-magic, while providing the same (or better) accuracy. © Springer-Verlag Berlin Heidelberg 2007
Estimating the mixing matrix in sparse component analysis based on converting a multiple dominant to a single dominant problem
, Article 7th International Conference on Independent Component Analysis (ICA) and Source Separation, ICA 2007, London, 9 September 2007 through 12 September 2007 ; Volume 4666 LNCS , 2007 , Pages 397-405 ; 03029743 (ISSN); 9783540744931 (ISBN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
Springer Verlag
2007
Abstract
We propose a new method for estimating the mixing matrix, A, in the linear model x(t) = As(t),t = 1,...,T, for the problem of underdetermined Sparse Component Analysis (SCA). Contrary to most previous algorithms, there can be more than one dominant source at each instant (we call it a "multiple dominant" problem). The main idea is to convert the multiple dominant problem to a series of single dominant problems, which may be solved by well-known methods. Each of these single dominant problems results in the determination of some columns of A. This results in a huge decrease in computations, which lets us to solve higher dimension problems that were not possible before. © Springer-Verlag...
A Minimization-Projection (MP) approach for blind separating convolutive mixtures
, Article Proceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing, Montreal, Que, 17 May 2004 through 21 May 2004 ; Volume 5 , 2004 , Pages V-533-V-536 ; 15206149 (ISSN) ; Jutten, C ; Nayebi, K ; Sharif University of Technology
2004
Abstract
In this paper, a new algorithm for blind source separation in convolutive mixtures, based on minimizing the mutual information of the outputs, is proposed. This minimization is done using a recently proposed Minimization-Projection (MP) approach for minimizing mutual information in a parametric model. Since the minimization step of the MP approach is proved to have no local minimum, it is expected that this new algorithm has good convergence behaviours
Causal Models in Representation Learning
, M.Sc. Thesis Sharif University of Technology ; Yassaeei Meybodi, Mohammad Hossein (Supervisor)
Abstract
Undoubtedly, machine learning and its practical applications have become essential topics in research and industry. Among these, representation learning holds special importance. The goal in this field is to find low-dimensional and meaningful representations of high-dimensional data that encapsulate essential information. Most existing methods focus only on the statistical correlations in data, which do not necessarily indicate real relationships between variables and can affect learning performance. This becomes even more problematic when the distribution of test data differs from the training data, as it makes the learned representations less generalizable. To tackle this problem, causal...
Blind source separation in nonlinear mixture for colored sources using signal derivatives
, Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 25 August 2015 through 28 August 2015 ; Volume 9237 , August , 2015 , Pages 193-200 ; 03029743 (ISSN) ; 9783319224817 (ISBN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
Springer Verlag
2015
Abstract
While Blind Source Separation (BSS) for linear mixtures has been well studied, the problem for nonlinear mixtures is still thought not to have a general solution. Each of the techniques proposed for solving BSS in nonlinear mixtures works mainly on specific models and cannot be generalized for many other realistic applications. Our approach in this paper is quite different and targets the general form of the problem. In this advance, we transform the nonlinear problem to a time-variant linear mixtures of the source derivatives. The proposed algorithm is based on separating the derivatives of the sources by a modified novel technique that has been developed and specialized for the problem,...
Image denoising using sparse representations
, Article 8th International Conference on Independent Component Analysis and Signal Separation, ICA 2009, Paraty, 15 March 2009 through 18 March 2009 ; Volume 5441 , 2009 , Pages 557-564 ; 03029743 (ISSN) ; Firouzi, H ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
2009
Abstract
The problem of removing white zero-mean Gaussian noise from an image is an interesting inverse problem to be investigated in this paper through sparse and redundant representations. However, finding the sparsest possible solution in the noise scenario was of great debate among the researchers. In this paper we make use of new approach to solve this problem and show that it is comparable with the state-of-art denoising approaches. © Springer-Verlag Berlin Heidelberg 2009
Face virtual pose generation using multi resolution subspaces
, Article 2008 International Symposium on Telecommunications, IST 2008, Tehran, 27 August 2008 through 28 August 2008 ; 2008 , Pages 629-633 ; 9781424427512 (ISBN) ; Rabiee, H. R ; Khansari, M ; Sharif University of Technology
2008
Abstract
In this paper a new method for face virtual pose generation is presented. The proposed method uses subspace image representation. The general problem of blurring is addressed by introducing a multi resolution time-frequency analysis to subspace image representation. The training gallery contains face images in two different poses. Undecimated Wavelet Transform is applied on all training face images in first pose and the corresponding images in the second pose to produce image subbands. Then, a new subspace is constructed for each subband in both poses. The mapping between two corresponding subbands of two poses is learnt using linear regression. The resulted mapping matrix is used to...
Estimating the mixing matrix in underdetermined Sparse Component Analysis (SCA) using consecutive independent component analysis (ICA)
, Article 16th European Signal Processing Conference, EUSIPCO 2008, Lausanne, 25 August 2008 through 29 August 2008 ; 2008 ; 22195491 (ISSN) ; Pad, P ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
2008
Abstract
One of the major problems in underdetermined Sparse Component Analysis (SCA) is the appropriate estimation of the mixing matrix, A, in the linear model x(t) = As(t), especially where more than one source is active at each instant of time (It is called 'multiple dominant problem'). Most of the previous algorithms were restricted to single dominant problem in which it is assumed that at each instant, there is at most one single dominant component. Moreover, because of high computational load, all present methods for multiple dominant problem are practical only for small scale cases (By 'small scale' we mean that the average number of active sources at each instant, k, is less than 5). In this...
Estimating the mixing matrix in Sparse Component Analysis (SCA) based on partial k-dimensional subspace clustering
, Article Neurocomputing ; Volume 71, Issue 10-12 , 2008 , Pages 2330-2343 ; 09252312 (ISSN) ; Hosein Mohimani, G ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
2008
Abstract
One of the major problems in underdetermined Sparse Component Analysis (SCA) in the field of (semi) Blind Source Separation (BSS) is the appropriate estimation of the mixing matrix, A, in the linear model X = AS, especially where more than one source is active at each instant of time. Most existing algorithms require the restriction that at each instant (i.e. in each column of the source matrix S), there is at most one single dominant component. Moreover, these algorithms require that the number of sources must be determined in advance. In this paper, we proposed a new algorithm for estimating the matrix A, which does not require the restriction of single dominant source at each instant....
Multichannel ECG and noise modeling: Application to maternal and fetal ECG signals
, Article Eurasip Journal on Advances in Signal Processing ; Volume 2007 , 2007 ; 11108657 (ISSN) ; Clifford, G. D ; Jutten, C ; Shamsollahi, M. B ; Sharif University of Technology
2007
Abstract
A three-dimensional dynamic model of the electrical activity of the heart is presented. The model is based on the single dipole model of the heart and is later related to the body surface potentials through a linear model which accounts for the temporal movements and rotations of the cardiac dipole, together with a realistic ECG noise model. The proposed model is also generalized to maternal and fetal ECG mixtures recorded from the abdomen of pregnant women in single and multiple pregnancies. The applicability of the model for the evaluation of signal processing algorithms is illustrated using independent component analysis. Considering the difficulties and limitations of recording long-term...
What ICA provides for ECG processing: Application to noninvasive fetal ECG extraction
, Article 6th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2006, Vancouver, BC, 27 August 2006 through 30 August 2006 ; 2006 , Pages 656-661 ; 0780397541 (ISBN); 9780780397545 (ISBN) ; Jutten, C ; Shamsollahi, M. B ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2006
Abstract
In recent studies, Independent Component Analysis (ICA) has been used for the analysis of multi-channel ECG recordings. However most of these works have been carried out from the signal processing perspective. In this work, the single dipole vector theory of the heart and the ECG dimensionality are studied from the source separation viewpoint. Based on this study, the interpretation of the components extracted from multi-channel ECG and maternal abdominal recordings, and their relationship with the vectorcardiogram representation of the cardiac dipole are presented. The results of this study can be used for the extraction of meaningful clinical indexes, based on ICA techniques. © 2006 IEEE