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jutten--christian
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Advances in Tensor Analysis with Applications in Text Mining
,
Ph.D. Dissertation
Sharif University of Technology
;
Babaiezadeh, Masoud
(Supervisor)
;
Comon, Pierre
(Co-Supervisor)
;
Jutten, Christian
(Co-Supervisor)
Abstract
Tensors or multi-way arrays are useful tools to identify unknown quantities thanks to the uniqueness of their decomposition. Tensor decompositions have been widely applied to obtain unknown components with physical meanings in many applications such as medical image and signal processing, hyperspectral images analysis, chemometrics, etc.In this thesis, we investigate the application of tensor decomposition for probability estimations, which are required for some targeted data/text mining tasks such as unsupervised clustering of data/documents. Besides criticizing the existing tensor decomposition algorithms for probability estimations, we propose to apply some proper constrained tensor...
Bilnd Source Separation in Nonlinear Mixtures
, Ph.D. Dissertation Sharif University of Technology ; Babaiezadeh, Massoud (Supervisor) ; Jutten, Christian (Co-Supervisor) ; Rivet, Bertrand (Co-Supervisor)
Abstract
Blind Source Separation (BSS) is a technique for estimating individual source components from their mixtures at multiple sensors, where the mixing model is unknown. Although it has been mathematically shown that for linear mixtures, under mild conditions, mutually independent sources can be reconstructed up to accepted ambiguities, there is not such theoretical basis for general nonlinear models. This is why there are relatively few resultsin the literature in this regard in the recent decades, which are focused on specific structured nonlinearities.In the present study, the problem is tackled using a novel approach utilizing temporal information of the signals. The original idea followed in...
Business ethics : a philosophical and behavioral approach
, Book
Springer International Publishing AG
2018
Separation of speech sources in under-determined case using SCA and time-frequency methods
, Article 2008 International Symposium on Telecommunications, IST 2008, Tehran, 27 August 2008 through 28 August 2008 ; 2008 , Pages 533-538 ; 9781424427512 (ISBN) ; Babaiezadeh, M ; Jutten, C ; Sharif University of Technology
2008
Abstract
This paper presents a new algorithm for Blind Source Separation (BSS) of Instantaneous speech mixtures in under-determined case. A demixing algorithm which exploits the sparsity of speech signals in the short time Fourier transform (STFT) domain is proposed. This algorithm combines the modified k-means clustering procedure involved in the Line Orientation Separation Technique (LOST) with Smoothed l0-norm minimization (SL0) method. First procedure along with a transformation into a sparse domain tries to estimate the mixing matrix, and the second method tries to extract the sources from the mixtures. Simulation results are presented and compared to the Degenerate Unmixing Estimation Technique...
Source estimation in noisy sparse component analysis
, Article 2007 15th International Conference onDigital Signal Processing, DSP 2007, Wales, 1 July 2007 through 4 July 2007 ; July , 2007 , Pages 219-222 ; 1424408822 (ISBN); 9781424408825 (ISBN) ; Babaiezadeh, M ; Jutten, C ; Sharif University of Technology
2007
Abstract
In this paper, a new algorithm for Sparse Component Analysis (SCA) in the noisy underdetermined case (i.e., with more sources than sensors) is presented. The solution obtained by the proposed algorithm is compared to the minimum l1 -norm solution achieved by Linear Programming (LP). Simulation results show that the proposed algorithm is approximately 10 dB better than the LP method with respect to the quality of the estimated sources. It is due to optimality of our solution (in the MAP sense) for source recovery in noisy underdetermined sparse component analysis in the case of spiky model for sparse sources and Gaussian noise. © 2007 IEEE
T wave alternans detection in ECG using extended kalman filter and dualrate EKF
, Article European Signal Processing Conference ; 1-5 September , 2014 , pp. 2500-2504 ; ISSN: 22195491 ; ISBN: 9780992862619 ; Shamsollahi, M. B ; Jutten, C ; Sharif University of Technology
2014
Abstract
T Wave Alternans (TWA) is considered as an indicator of Sudden Cardiac Death (SCD). In this paper for TWA detection, a method based on a nonlinear dynamic model is presented. For estimating the model parameters, we use an Extended Kalman Filter (EKF). We propose EKF6 and dualrate EKF6 approaches. Dualrate EKF is suitable for modeling the states which are not updated in all time instances. Quantitative and qualitative evaluations of the proposed method have been done on TWA challenge database. We compare our method with that proposed by Sieed et al. in TWA challenge 2008. We also compare our method with our previous proposed approach (EKF25-4obs). Results show that the proposed method can...
Learning overcomplete dictionaries based on atom-by-atom updating
, Article IEEE Transactions on Signal Processing ; Volume 62, Issue 4 , 15 February , 2014 , Pages 883-891 ; ISSN: 1053587X ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
2014
Abstract
A dictionary learning algorithm learns a set of atoms from some training signals in such a way that each signal can be approximated as a linear combination of only a few atoms. Most dictionary learning algorithms use a two-stage iterative procedure. The first stage is to sparsely approximate the training signals over the current dictionary. The second stage is the update of the dictionary. In this paper we develop some atom-by-atom dictionary learning algorithms, which update the atoms sequentially. Specifically, we propose an efficient alternative to the well-known K-SVD algorithm, and show by various experiments that the proposed algorithm is much faster than K-SVD while its results are...
A projected gradient-based algorithm to unmix hyperspectral data
, Article European Signal Processing Conference ; 2012 , Pages 2482-2486 ; 22195491 (ISSN) ; 9781467310680 (ISBN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
2012
Abstract
This paper presents a method to solve hyperspectral unmixing problem based on the well-known linear mixing model. Hyperspectral unmixing is to decompose observed spectrum of a mixed pixel into its constituent spectra and a set of corresponding abundances. We use Nonnegative Matrix Factorization (NMF) to solve the problem in a single step. The proposed method is based on a projected gradient NMF algorithm. Moreover, we modify the NMF algorithm by adding a penalty term to include also the statistical independence of abundances. At the end, the performance of the method is compared to two other algorithms using both real and synthetic data. In these experiments, the algorithm shows interesting...
Dictionary learning for sparse representation: A novel approach
, Article IEEE Signal Processing Letters ; Volume 20, Issue 12 , 2013 , Pages 1195-1198 ; 10709908 (ISSN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
2013
Abstract
A dictionary learning problem is a matrix factorization in which the goal is to factorize a training data matrix, Y, as the product of a dictionary, D, and a sparse coefficient matrix, X, as follows, Y ≈ DX. Current dictionary learning algorithms minimize the representation error subject to a constraint on D (usually having unit column-norms) and sparseness of X. The resulting problem is not convex with respect to the pair (D, X). In this letter, we derive a first order series expansion formula for the factorization, DX. The resulting objective function is jointly convex with respect to D and X. We simply solve the resulting problem using alternating minimization and apply some of the...
ECG fiducial points extraction by extended Kalman filtering
, Article 2013 36th International Conference on Telecommunications and Signal Processing, TSP 2013 ; 2013 , Pages 628-632 ; 9781479904044 (ISBN) ; Shamsollahi, M. B ; Jutten, C ; Sharif University of Technology
2013
Abstract
Most of the clinically useful information in Electrocardiogram (ECG) signal can be obtained from the intervals, amplitudes and wave shapes (morphologies). The automatic detection of ECG waves is important to cardiac disease diagnosis. In this paper, we propose an efficient method for extraction of characteristic points of ECG. The method is based on a nonlinear dynamic model, previously introduced for generation of synthetic ECG signals. For estimating the parameters of model, we use an Extendend Kalman Filter (EKF). By introducing a simple AR model for each of the dynamic parameters of Gaussian functions in model and considering separate states for ECG waves, the new EKF structure was...
Fiducial points extraction and characteristicwaves detection in ECG signal using a model-based bayesian framework
, Article ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings ; 2013 , Pages 1257-1261 ; 15206149 (ISSN) ; 9781479903566 (ISBN) ; Shamsollahi, M. B ; Jutten, C ; Sharif University of Technology
2013
Abstract
The automatic detection of Electrocardiogram (ECG) waves is important to cardiac disease diagnosis. A good performance of an automatic ECG analyzing system depends heavily upon the accurate and reliable detection of QRS complex, as well as P and T waves. In this paper, we propose an efficient method for extraction of characteristic points of ECG signal. The method is based on a nonlinear dynamic model, previously introduced for generation of synthetic ECG signals. For estimating the parameters of model, we use an Extendend Kalman Filter (EKF). By introducing a simple AR model for each of the dynamic parameters of Gaussian functions in model and considering separate states for ECG waves, the...
A new algorithm for learning overcomplete dictionaries
, Article European Signal Processing Conference, Marrakech ; Sept , 2013 , Page(s): 1 - 4 ; 22195491 (ISSN) ; 9780992862602 (ISBN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
European Signal Processing Conference, EUSIPCO
2013
Abstract
In this paper, we propose a new algorithm for learning overcomplete dictionaries. The proposed algorithm is actually a new approach for optimizing a recently proposed cost function for dictionary learning. This cost function is regularized with a term that encourages low similarity between different atoms. While the previous approach needs to run an iterative limited-memory BFGS (l-BFGS) algorithm at each iteration of another iterative algorithm, our approach uses a closedform formula. Experimental results on reconstruction of a true underlying dictionary and designing a sparsifying dictionary for a class of autoregressive signals show that our approach results in both better quality and...
On the achievability of Cramér-Rao bound in noisy compressed sensing
, Article IEEE Transactions on Signal Processing ; Volume 60, Issue 1 , 2012 , Pages 518-526 ; 1053587X (ISSN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
2012
Abstract
Recently, it has been proved in Babadi [B. Babadi, N. Kalouptsidis, and V. Tarokh, "Asymptotic achievability of the Cramér-Rao bound for noisy compressive sampling", IEEE Trans. Signal Process., vol. 57, no. 3, pp. 1233-1236, 2009] that in noisy compressed sensing, a joint typical estimator can asymptotically achieve the Cramér-Rao lower bound of the problem. To prove this result, Babadi used a lemma, which is provided in Akçakaya and Tarokh [M. Akçakaya and V. Trarokh, "Shannon theoretic limits on noisy compressive sampling", IEEE Trans. Inf. Theory, vol. 56, no. 1, pp. 492-504, 2010] that comprises the main building block of the proof. This lemma is based on the assumption of Gaussianity...
Error correction via smoothed L0-norm recovery
, Article IEEE Workshop on Statistical Signal Processing Proceedings, 28 June 2011 through 30 June 2011 ; June , 2011 , Pages 289-292 ; 9781457705700 (ISBN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
2011
Abstract
Channel coding has been considered as a classical approach to overcome corruptions occurring in some elements of input signal which may lead to loss of some information. Proper redundancies are added to the input signal to improve the capability of detecting or even correcting the corrupted signal. A similar scenario may happen dealing with real-field numbers rather than finite-fields. This paper considers a way to reconstruct an exact version of a corrupted signal by using an encoded signal with proper number of redundancies. The proposed algorithm uses Graduated Non-Convexity method beside using a smoothed function instead of 0-norm to correct all the corrupted elements. Simulations show...
Regularized low-coherence overcomplete dictionary learning for sparse signal decomposition
, Article European Signal Processing Conference, 28 August 2016 through 2 September 2016 ; Volume 2016-November , 2016 , Pages 369-373 ; 22195491 (ISSN) ; 9780992862657 (ISBN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
European Signal Processing Conference, EUSIPCO
2016
Abstract
This paper deals with learning an overcomplete set of atoms that have low mutual coherence. To this aim, we propose a new dictionary learning (DL) problem that enables a control on the amounts of the decomposition error and the mutual coherence of the atoms of the dictionary. Unlike existing methods, our new problem directly incorporates the mutual coherence term into the usual DL problem as a regularizer. We also propose an efficient algorithm to solve the new problem. Our new algorithm uses block coordinate descent, and updates the dictionary atom-by-atom, leading to closed-form solutions. We demonstrate the superiority of our new method over existing approaches in learning low-coherence...
An alternating minimization method for sparse channel estimation
, Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 27 September 2010 through 30 September 2010, St. Malo ; Volume 6365 LNCS , 2010 , Pages 319-327 ; 03029743 (ISSN) ; 364215994X (ISBN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
2010
Abstract
The problem of estimating a sparse channel, i.e. a channel with a few non-zero taps, appears in many fields of communication including acoustic underwater or wireless transmissions. In this paper, we have developed an algorithm based on Iterative Alternating Minimization technique which iteratively detects the location and the value of the channel taps. In fact, at each iteration we use an approximate Maximum A posteriori Probability (MAP) scheme for detection of the taps, while a least square method is used for estimating the values of the taps at each iteration. For approximate MAP detection, we have proposed three different methods leading to three variants for our algorithm. Finally, we...
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,...
A MAP-Based order estimation procedure for Sparse channel estimation
, Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 25 2015 through 28 August 2015 ; Volume 9237 , August , 2015 , Pages 344-351 ; 03029743 (ISSN) ; 9783319224817 (ISBN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
Springer Verlag
2015
Abstract
Recently, there has been a growing interest in estimation of sparse channels as they are observed in underwater acoustic and ultrawideband channels. In this paper we present a new Bayesian sparse channel estimation (SCE) algorithm that, unlike traditional SCE methods, exploits noise statistical information to improve the estimates. The proposed method uses approximate maximum a posteriori probability (MAP) to detect the non-zero channel tap locations while least square estimation is used to determine the values of the channel taps. Computer simulations shows that the proposed algorithm outperforms the existing algorithms in terms of normalized mean squared error (NMSE) and approaches...
Compressed sensing block MAP-LMS adaptive filter for sparse channel estimation and a Bayesian Cramer-Rao bound
, Article Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009, 2 September 2009 through 4 September 2009 ; 2009 ; 9781424449484 (ISBN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
2009
Abstract
This paper suggests to use a Block MAP-LMS (BMAP-LMS) adaptive filter instead of an Adaptive Filter called MAP-LMS for estimating the sparse channels. Moreover to faster convergence than MAP-LMS, this block-based adaptive filter enables us to use a compressed sensing version of it which exploits the sparsity of the channel outputs to reduce the sampling rate of the received signal and to alleviate the complexity of the BMAP-LMS. Our simulations show that our proposed algorithm has faster convergence and less final MSE than MAP-LMS, while it is more complex than MAP-LMS. Moreover, some lower bounds for sparse channel estimation is discussed. Specially, a Cramer-Rao bound and a Bayesian...
Blind separation of bilinear mixtures using mutual information minimization
, Article Machine Learning for Signal Processing XIX - Proceedings of the 2009 IEEE Signal Processing Society Workshop, MLSP 2009, 2 September 2009 through 4 September 2009, Grenoble ; 2009 ; 9781424449484 (ISBN) ; Babaie Zadeh, M ; Jutten, C ; Sharif University of Technology
2009
Abstract
In this paper an approach for blind source separation in bilinear (or linear quadratic) mixtures is presented. The proposed algorithm employs the same recurrent structure as [Hosseini and Deville, 2003) for separating these mixtures . However, instead of maximal likelihood, our algorithm is based on minimizing the mutual information of the outputs for recovering the independent components. Simulation results show the efficiency of the proposed algorithm. © 2009 IEEE