The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
Zoltán Szabó - One of the best experts on this subject based on the ideXlab platform.
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ESANN - Autoregressive Independent Process Analysis with Missing Observations
2020Co-Authors: Zoltán SzabóAbstract:The goal of this paper is to search for Independent multi- dimensional Processes subject to missing and mixed observations. The corresponding cocktail-party problem has a number of successful applica- tions, however, the case of missing observations has been worked out only for the simplest Independent Component Analysis (ICA) task, where the hidden Processes (i) are one-dimensional, and (ii) signal generation in time is Independent and identically distributed (i.i.d.). Here, the missing ob- servation situation is extended to Processes with (i) autoregressive (AR) dynamics and (ii) multidimensional driving sources. Performance of the solution method is illustrated by numerical examples.
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Complex Independent Process analysis
Acta Cybernetica, 2020Co-Authors: Zoltán Szabó, Andras LorinczAbstract:We present a general framework for the search of hidden Independent Processes in the complex domain. The task is to estimate the hidden Independent multidimensional complex-valued components observing only the mixture of the Processes driven by them. In our model (i) the hidden Independent Processes can be multidimensional, they may be subject to (ii) moving averaging, or may evolve in an autoregressive manner, or (iii) they can be non-stationary. These assumptions are covered by integrated autoregressive moving average Processes and thus our task is to solve their complex extensions. We show how to reduce the undercomplete version of complex integrated autoregressive moving average Processes to real Independent subspace analysis that we can solve. Simulations illustrate the working of the algorithm.
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EUSIPCO - Nonparametric Independent Process analysis
2011Co-Authors: Zoltán Szabó, Barnabás PóczosAbstract:Linear dynamical systems are widely used tools to model stochastic time Processes, but they have severe limitations; they assume linear dynamics with Gaussian driving noise. Independent component analysis (ICA) aims to weaken these limitations by allowing Independent, non-Gaussian sources in the model. Independent subspace analysis (ISA), an important generalization of ICA, has proven to be successful in many source separation applications. Still, the general ISA problem of separating sources with nonparametric dynamics has been hardly touched in the literature yet. The goal of this paper is to extend ISA to the case of (i) nonparametric, asymptotically stationary source dynamics and (ii) unknown source component dimensions. We make use of functional autoregressive (fAR) Processes to model the temporal evolution of the hidden sources. An extension of the well-known ISA separation principle is derived for the solution of the introduced fAR Independent Process analysis (fAR-IPA) task. By applying fAR identification we reduce the problem to ISA. The Nadaraya-Watson kernel regression technique is adapted to obtain strongly consistent fAR estimation. We illustrate the efficiency of the fAR-IPA approach by numerical examples and demonstrate that in this framework our method is superior to standard linear dynamical system based estimators.
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Nonparametric Independent Process analysis
2011 19th European Signal Processing Conference, 2011Co-Authors: Zoltán Szabó, Barnabás PóczosAbstract:Linear dynamical systems are widely used tools to model stochastic time Processes, but they have severe limitations; they assume linear dynamics with Gaussian driving noise. Independent component analysis (ICA) aims to weaken these limitations by allowing Independent, non-Gaussian sources in the model. Independent subspace analysis (ISA), an important generalization of ICA, has proven to be successful in many source separation applications. Still, the general ISA problem of separating sources with nonparametric dynamics has been hardly touched in the literature yet. The goal of this paper is to extend ISA to the case of (i) nonparametric, asymptotically stationary source dynamics and (ii) unknown source component dimensions. We make use of functional autoregressive (fAR) Processes to model the temporal evolution of the hidden sources. An extension of the well-known ISA separation principle is derived for the solution of the introduced fAR Independent Process analysis (fAR-IPA) task. By applying fAR identification we reduce the problem to ISA. The Nadaraya-Watson kernel regression technique is adapted to obtain strongly consistent fAR estimation. We illustrate the efficiency of the fAR-IPA approach by numerical examples and demonstrate that in this framework our method is superior to standard linear dynamical system based estimators.
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Towards Nonstationary, Nonparametric Independent Process Analysis with Unknown Source Component Dimensions
arXiv: Methodology, 2010Co-Authors: Zoltán SzabóAbstract:The goal of this paper is to extend Independent subspace analysis (ISA) to the case of (i) nonparametric, not strictly stationary source dynamics and (ii) unknown source component dimensions. We make use of functional autoregressive (fAR) Processes to model the temporal evolution of the hidden sources. An extension of the ISA separation principle--which states that the ISA problem can be solved by traditional Independent component analysis (ICA) and clustering of the ICA elements--is derived for the solution of the defined fAR Independent Process analysis task (fAR-IPA): applying fAR identification we reduce the problem to ISA. A local averaging approach, the Nadaraya-Watson kernel regression technique is adapted to obtain strongly consistent fAR estimation. We extend the Amari-index to different dimensional components and illustrate the efficiency of the fAR-IPA approach by numerical examples.
Isabel Sola - One of the best experts on this subject based on the ideXlab platform.
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transmissible gastroenteritis coronavirus genome packaging signal is located at the 5 end of the genome and promotes viral rna incorporation into virions in a replication Independent Process
Journal of Virology, 2013Co-Authors: Lucia Morales, Pedro A Mateosgomez, Carmen Capiscol, Lorena Palacio, Luis Enjuanes, Isabel SolaAbstract:Preferential RNA packaging in coronaviruses involves the recognition of viral genomic RNA, a crucial Process for viral particle morphogenesis mediated by RNA-specific sequences, known as packaging signals. An essential packaging signal component of transmissible gastroenteritis coronavirus (TGEV) has been further delimited to the first 598 nucleotides (nt) from the 5' end of its RNA genome, by using recombinant viruses transcribing subgenomic mRNA that included potential packaging signals. The integrity of the entire sequence domain was necessary because deletion of any of the five structural motifs defined within this region abrogated specific packaging of this viral RNA. One of these RNA motifs was the stem-loop SL5, a highly conserved motif in coronaviruses located at nucleotide positions 106 to 136. Partial deletion or point mutations within this motif also abrogated packaging. Using TGEV-derived defective minigenomes replicated in trans by a helper virus, we have shown that TGEV RNA packaging is a replication-Independent Process. Furthermore, the last 494 nt of the genomic 3' end were not essential for packaging, although this region increased packaging efficiency. TGEV RNA sequences identified as necessary for viral genome packaging were not sufficient to direct packaging of a heterologous sequence derived from the green fluorescent protein gene. These results indicated that TGEV genome packaging is a complex Process involving many factors in addition to the identified RNA packaging signal. The identification of well-defined RNA motifs within the TGEV RNA genome that are essential for packaging will be useful for designing packaging-deficient biosafe coronavirus-derived vectors and providing new targets for antiviral therapies.
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Transmissible Gastroenteritis Coronavirus Genome Packaging Signal Is Located at the 5′ End of the Genome and Promotes Viral RNA Incorporation into Virions in a Replication-Independent Process
Journal of Virology, 2013Co-Authors: Lucia Morales, Carmen Capiscol, Lorena Palacio, Luis Enjuanes, Pedro A. Mateos-gomez, Isabel SolaAbstract:ABSTRACT Preferential RNA packaging in coronaviruses involves the recognition of viral genomic RNA, a crucial Process for viral particle morphogenesis mediated by RNA-specific sequences, known as packaging signals. An essential packaging signal component of transmissible gastroenteritis coronavirus (TGEV) has been further delimited to the first 598 nucleotides (nt) from the 5′ end of its RNA genome, by using recombinant viruses transcribing subgenomic mRNA that included potential packaging signals. The integrity of the entire sequence domain was necessary because deletion of any of the five structural motifs defined within this region abrogated specific packaging of this viral RNA. One of these RNA motifs was the stem-loop SL5, a highly conserved motif in coronaviruses located at nucleotide positions 106 to 136. Partial deletion or point mutations within this motif also abrogated packaging. Using TGEV-derived defective minigenomes replicated in trans by a helper virus, we have shown that TGEV RNA packaging is a replication-Independent Process. Furthermore, the last 494 nt of the genomic 3′ end were not essential for packaging, although this region increased packaging efficiency. TGEV RNA sequences identified as necessary for viral genome packaging were not sufficient to direct packaging of a heterologous sequence derived from the green fluorescent protein gene. These results indicated that TGEV genome packaging is a complex Process involving many factors in addition to the identified RNA packaging signal. The identification of well-defined RNA motifs within the TGEV RNA genome that are essential for packaging will be useful for designing packaging-deficient biosafe coronavirus-derived vectors and providing new targets for antiviral therapies.
Andras Lorincz - One of the best experts on this subject based on the ideXlab platform.
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Complex Independent Process analysis
Acta Cybernetica, 2020Co-Authors: Zoltán Szabó, Andras LorinczAbstract:We present a general framework for the search of hidden Independent Processes in the complex domain. The task is to estimate the hidden Independent multidimensional complex-valued components observing only the mixture of the Processes driven by them. In our model (i) the hidden Independent Processes can be multidimensional, they may be subject to (ii) moving averaging, or may evolve in an autoregressive manner, or (iii) they can be non-stationary. These assumptions are covered by integrated autoregressive moving average Processes and thus our task is to solve their complex extensions. We show how to reduce the undercomplete version of complex integrated autoregressive moving average Processes to real Independent subspace analysis that we can solve. Simulations illustrate the working of the algorithm.
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Controlled Complete ARMA Independent Process Analysis
2009 International Joint Conference on Neural Networks, 2009Co-Authors: Zoltán Szabó, Andras LorinczAbstract:In this paper we address the controlled complete AutoRegressive Moving Average Independent Process Analysis (ARMAX-IPA; X-exogenous input or control) problem, which is a generalization of the blind subspace deconvolution (BSSD) task. Compared to our previous work that dealt with the undercomplete situation, (i) here we extend the theory to complete systems, (ii) allow an autoregressive part to be present, (iii) and include exogenous control. We investigate the case when the observed signal is a linear mixture of Independent multidimensional ARMA Processes that can be controlled. Our objective is to estimate the ARMA Processes, their driving noises as well as the mixing. We aim efficient estimation by choosing suitable control values. For the optimal choice of the control we adapt the D-optimality principle, also known as the dasiaInfoMax methodpsila. We solve the problem by reducing it to a fully observable D-optimal ARX task and Independent Subspace Analysis (ISA) that we can solve. Numerical examples illustrate the efficiency of the proposed method.
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IJCNN - Controlled Complete ARMA Independent Process Analysis
2009 International Joint Conference on Neural Networks, 2009Co-Authors: Zoltán Szabó, Andras LorinczAbstract:In this paper we address the controlled complete AutoRegressive Moving Average Independent Process Analysis (ARMAX-IPA; X-exogenous input or control) problem, which is a generalization of the Blind SubSpace Deconvolution (BSSD) task. Compared to our previous work that dealt with the undercomplete situation, (i) here we extend the theory to complete systems, (ii) allow an autoregressive part to be present, (iii) and include exogenous control. We investigate the case when the observed signal is a linear mixture of Independent multidimensional ARMA Processes that can be controlled. Our objective is to estimate the ARMA Processes, their driving noises as well as the mixing. We aim efficient estimation by choosing suitable control values. For the optimal choice of the control we adapt the D-optimality principle, also known as the ‘InfoMax method’. We solve the problem by reducing it to a fully observable D-optimal ARX task and Independent Subspace Analysis (ISA) that we can solve. Numerical examples illustrate the efficiency of the proposed method.
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ICA - Independent Process analysis without a priori dimensional information
Independent Component Analysis and Signal Separation, 2007Co-Authors: Barnabás Póczos, Zoltán Szabó, Melinda Kiszlinger, Andras LorinczAbstract:Recently, several algorithms have been proposed for Independent subspace analysis where hidden variables are i.i.d. Processes. We show that these methods can be extended to certain AR, MA, ARMA and ARIMA tasks. Central to our paper is that we introduce a cascade of algorithms, which aims to solve these tasks without previous knowledge about the number and the dimensions of the hidden Processes. Our claim is supported by numerical simulations. As an illustrative application where the dimensions of the hidden variables are unknown, we search for subspaces of facial components.
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Independent Process Analysis without A Priori Dimensional Information
arXiv: Statistics Theory, 2007Co-Authors: Barnabás Póczos, Zoltán Szabó, Melinda Kiszlinger, Andras LorinczAbstract:Recently, several algorithms have been proposed for Independent subspace analysis where hidden variables are i.i.d. Processes. We show that these methods can be extended to certain AR, MA, ARMA and ARIMA tasks. Central to our paper is that we introduce a cascade of algorithms, which aims to solve these tasks without previous knowledge about the number and the dimensions of the hidden Processes. Our claim is supported by numerical simulations. As a particular application, we search for subspaces of facial components.
Barnabás Póczos - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - Nonparametric Independent Process analysis
2011Co-Authors: Zoltán Szabó, Barnabás PóczosAbstract:Linear dynamical systems are widely used tools to model stochastic time Processes, but they have severe limitations; they assume linear dynamics with Gaussian driving noise. Independent component analysis (ICA) aims to weaken these limitations by allowing Independent, non-Gaussian sources in the model. Independent subspace analysis (ISA), an important generalization of ICA, has proven to be successful in many source separation applications. Still, the general ISA problem of separating sources with nonparametric dynamics has been hardly touched in the literature yet. The goal of this paper is to extend ISA to the case of (i) nonparametric, asymptotically stationary source dynamics and (ii) unknown source component dimensions. We make use of functional autoregressive (fAR) Processes to model the temporal evolution of the hidden sources. An extension of the well-known ISA separation principle is derived for the solution of the introduced fAR Independent Process analysis (fAR-IPA) task. By applying fAR identification we reduce the problem to ISA. The Nadaraya-Watson kernel regression technique is adapted to obtain strongly consistent fAR estimation. We illustrate the efficiency of the fAR-IPA approach by numerical examples and demonstrate that in this framework our method is superior to standard linear dynamical system based estimators.
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Nonparametric Independent Process analysis
2011 19th European Signal Processing Conference, 2011Co-Authors: Zoltán Szabó, Barnabás PóczosAbstract:Linear dynamical systems are widely used tools to model stochastic time Processes, but they have severe limitations; they assume linear dynamics with Gaussian driving noise. Independent component analysis (ICA) aims to weaken these limitations by allowing Independent, non-Gaussian sources in the model. Independent subspace analysis (ISA), an important generalization of ICA, has proven to be successful in many source separation applications. Still, the general ISA problem of separating sources with nonparametric dynamics has been hardly touched in the literature yet. The goal of this paper is to extend ISA to the case of (i) nonparametric, asymptotically stationary source dynamics and (ii) unknown source component dimensions. We make use of functional autoregressive (fAR) Processes to model the temporal evolution of the hidden sources. An extension of the well-known ISA separation principle is derived for the solution of the introduced fAR Independent Process analysis (fAR-IPA) task. By applying fAR identification we reduce the problem to ISA. The Nadaraya-Watson kernel regression technique is adapted to obtain strongly consistent fAR estimation. We illustrate the efficiency of the fAR-IPA approach by numerical examples and demonstrate that in this framework our method is superior to standard linear dynamical system based estimators.
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Auto-regressive Independent Process analysis without combinatorial efforts
Pattern Analysis and Applications, 2010Co-Authors: Zoltán Szabó, Barnabás Póczos, András LőrinczAbstract:We treat the problem of searching for hidden multi-dimensional Independent auto-regressive Processes (auto-regressive Independent Process analysis, AR-IPA). Independent subspace analysis (ISA) can be used to solve the AR-IPA task. The so-called separation theorem simplifies the ISA task considerably: the theorem enables one to reduce the task to one-dimensional blind source separation task followed by the grouping of the coordinates. However, the grouping of the coordinates still involves two types of combinatorial problems: (a) the number of the Independent subspaces and their dimensions, and then (b) the permutation of the estimated coordinates are to be determined. Here, we generalize the separation theorem. We also show a non-combinatorial procedure, which—under certain conditions—can treat these two combinatorial problems. Numerical simulations have been conducted. We investigate problems that fulfill sufficient conditions of the theory and also others that do not. The success of the numerical simulations indicates that further generalizations of the separation theorem may be feasible.
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ICA - Independent Process analysis without a priori dimensional information
Independent Component Analysis and Signal Separation, 2007Co-Authors: Barnabás Póczos, Zoltán Szabó, Melinda Kiszlinger, Andras LorinczAbstract:Recently, several algorithms have been proposed for Independent subspace analysis where hidden variables are i.i.d. Processes. We show that these methods can be extended to certain AR, MA, ARMA and ARIMA tasks. Central to our paper is that we introduce a cascade of algorithms, which aims to solve these tasks without previous knowledge about the number and the dimensions of the hidden Processes. Our claim is supported by numerical simulations. As an illustrative application where the dimensions of the hidden variables are unknown, we search for subspaces of facial components.
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Independent Process Analysis without A Priori Dimensional Information
arXiv: Statistics Theory, 2007Co-Authors: Barnabás Póczos, Zoltán Szabó, Melinda Kiszlinger, Andras LorinczAbstract:Recently, several algorithms have been proposed for Independent subspace analysis where hidden variables are i.i.d. Processes. We show that these methods can be extended to certain AR, MA, ARMA and ARIMA tasks. Central to our paper is that we introduce a cascade of algorithms, which aims to solve these tasks without previous knowledge about the number and the dimensions of the hidden Processes. Our claim is supported by numerical simulations. As a particular application, we search for subspaces of facial components.
Lucia Morales - One of the best experts on this subject based on the ideXlab platform.
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transmissible gastroenteritis coronavirus genome packaging signal is located at the 5 end of the genome and promotes viral rna incorporation into virions in a replication Independent Process
Journal of Virology, 2013Co-Authors: Lucia Morales, Pedro A Mateosgomez, Carmen Capiscol, Lorena Palacio, Luis Enjuanes, Isabel SolaAbstract:Preferential RNA packaging in coronaviruses involves the recognition of viral genomic RNA, a crucial Process for viral particle morphogenesis mediated by RNA-specific sequences, known as packaging signals. An essential packaging signal component of transmissible gastroenteritis coronavirus (TGEV) has been further delimited to the first 598 nucleotides (nt) from the 5' end of its RNA genome, by using recombinant viruses transcribing subgenomic mRNA that included potential packaging signals. The integrity of the entire sequence domain was necessary because deletion of any of the five structural motifs defined within this region abrogated specific packaging of this viral RNA. One of these RNA motifs was the stem-loop SL5, a highly conserved motif in coronaviruses located at nucleotide positions 106 to 136. Partial deletion or point mutations within this motif also abrogated packaging. Using TGEV-derived defective minigenomes replicated in trans by a helper virus, we have shown that TGEV RNA packaging is a replication-Independent Process. Furthermore, the last 494 nt of the genomic 3' end were not essential for packaging, although this region increased packaging efficiency. TGEV RNA sequences identified as necessary for viral genome packaging were not sufficient to direct packaging of a heterologous sequence derived from the green fluorescent protein gene. These results indicated that TGEV genome packaging is a complex Process involving many factors in addition to the identified RNA packaging signal. The identification of well-defined RNA motifs within the TGEV RNA genome that are essential for packaging will be useful for designing packaging-deficient biosafe coronavirus-derived vectors and providing new targets for antiviral therapies.
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Transmissible Gastroenteritis Coronavirus Genome Packaging Signal Is Located at the 5′ End of the Genome and Promotes Viral RNA Incorporation into Virions in a Replication-Independent Process
Journal of Virology, 2013Co-Authors: Lucia Morales, Carmen Capiscol, Lorena Palacio, Luis Enjuanes, Pedro A. Mateos-gomez, Isabel SolaAbstract:ABSTRACT Preferential RNA packaging in coronaviruses involves the recognition of viral genomic RNA, a crucial Process for viral particle morphogenesis mediated by RNA-specific sequences, known as packaging signals. An essential packaging signal component of transmissible gastroenteritis coronavirus (TGEV) has been further delimited to the first 598 nucleotides (nt) from the 5′ end of its RNA genome, by using recombinant viruses transcribing subgenomic mRNA that included potential packaging signals. The integrity of the entire sequence domain was necessary because deletion of any of the five structural motifs defined within this region abrogated specific packaging of this viral RNA. One of these RNA motifs was the stem-loop SL5, a highly conserved motif in coronaviruses located at nucleotide positions 106 to 136. Partial deletion or point mutations within this motif also abrogated packaging. Using TGEV-derived defective minigenomes replicated in trans by a helper virus, we have shown that TGEV RNA packaging is a replication-Independent Process. Furthermore, the last 494 nt of the genomic 3′ end were not essential for packaging, although this region increased packaging efficiency. TGEV RNA sequences identified as necessary for viral genome packaging were not sufficient to direct packaging of a heterologous sequence derived from the green fluorescent protein gene. These results indicated that TGEV genome packaging is a complex Process involving many factors in addition to the identified RNA packaging signal. The identification of well-defined RNA motifs within the TGEV RNA genome that are essential for packaging will be useful for designing packaging-deficient biosafe coronavirus-derived vectors and providing new targets for antiviral therapies.
