The Experts below are selected from a list of 237 Experts worldwide ranked by ideXlab platform
Shrikanth S. Narayanan - One of the best experts on this subject based on the ideXlab platform.
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simplified supervised i vector modeling with application to robust and efficient language identification and speaker verification
Computer Speech & Language, 2014Co-Authors: Ming Li, Shrikanth S. NarayananAbstract:Abstract This paper presents a simplified and supervised i-vector modeling approach with applications to robust and efficient language identification and speaker verification. First, by concatenating the label vector and the linear regression Matrix at the end of the mean supervector and the i-vector Factor Loading Matrix, respectively, the traditional i-vectors are extended to label-regularized supervised i-vectors. These supervised i-vectors are optimized to not only reconstruct the mean supervectors well but also minimize the mean square error between the original and the reconstructed label vectors to make the supervised i-vectors become more discriminative in terms of the label information. Second, Factor analysis (FA) is performed on the pre-normalized centered GMM first order statistics supervector to ensure each gaussian component's statistics sub-vector is treated equally in the FA, which reduces the computational cost by a Factor of 25 in the simplified i-vector framework. Third, since the entire Matrix inversion term in the simplified i-vector extraction only depends on one single variable (total frame number), we make a global table of the resulting matrices against the frame numbers’ log values. Using this lookup table, each utterance's simplified i-vector extraction is further sped up by a Factor of 4 and suffers only a small quantization error. Finally, the simplified version of the supervised i-vector modeling is proposed to enhance both the robustness and efficiency. The proposed methods are evaluated on the DARPA RATS dev2 task, the NIST LRE 2007 general task and the NIST SRE 2010 female condition 5 task for noisy channel language identification, clean channel language identification and clean channel speaker verification, respectively. For language identification on the DARPA RATS, the simplified supervised i-vector modeling achieved 2%, 16%, and 7% relative equal error rate (EER) reduction on three different feature sets and sped up by a Factor of more than 100 against the baseline i-vector method for the 120 s task. Similar results were observed on the NIST LRE 2007 30 s task with 7% relative average cost reduction. Results also show that the use of Gammatone frequency cepstral coefficients, Mel-frequency cepstral coefficients and spectro-temporal Gabor features in conjunction with shifted-delta-cepstral features improves the overall language identification performance significantly. For speaker verification, the proposed supervised i-vector approach outperforms the i-vector baseline by relatively 12% and 7% in terms of EER and norm old minDCF values, respectively.
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Simplified and supervised i-vector modeling for speaker age regression
2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014Co-Authors: Prashanth Gurunath Shivakumar, Ming Li, Vedant Dhandhania, Shrikanth S. NarayananAbstract:We propose a simplified and supervised i-vector modeling scheme for the speaker age regression task. The supervised i-vector is obtained by concatenating the label vector and the linear regression Matrix at the end of the mean super-vector and the i-vector Factor Loading Matrix, respectively. Different label vector designs are proposed to increase the robustness of the supervised i-vector models. Finally, Support Vector Regression (SVR) is deployed to estimate the age of the speakers. The proposed method outperforms the conventional i-vector baseline for speaker age estimation. A relative 2.4% decrease in Mean Absolute Error and 3.33% increase in correlation coefficient is achieved using supervised i-vector modeling using different label designs on the NIST SRE 2008 dataset male part.
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ICASSP - SIMPLIFIED AND SUPERVISED I-VECTOR MODELING FOR SPEAKER AGE REGRESSION
2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014Co-Authors: Prashanth Gurunath Shivakumar, Ming Li, Vedant Dhandhania, Shrikanth S. NarayananAbstract:We propose a simplified and supervised i-vector modeling scheme for the speaker age regression task. The supervised i-vector is obtained by concatenating the label vector and the linear regression Matrix at the end of the mean super-vector and the i-vector Factor Loading Matrix, respectively. Different label vector designs are proposed to increase the robustness of the supervised i-vector models. Finally, Support Vector Regression (SVR) is deployed to estimate the age of the speakers. The proposed method outperforms the conventional i-vector baseline for speaker age estimation. A relative 2.4% decrease in Mean Absolute Error and 3.33% increase in correlation coefficient is achieved using supervised i-vector modeling using different label designs on the NIST SRE 2008 dataset male part.
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Speaker verification using simplified and supervised i-vector modeling
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Ming Li, Andreas Tsiartas, Maarten Van Segbroeck, Shrikanth S. NarayananAbstract:This paper presents a simplified and supervised i-vector modeling framework that is applied in the task of robust and efficient speaker verification (SRE). First, by concatenating the mean supervector and the i-vector Factor Loading Matrix with respectively the label vector and the linear classifier Matrix, the traditional i-vectors are then extended to label-regularized supervised i-vectors. These supervised i-vectors are optimized to not only reconstruct the mean supervectors well but also minimize the mean squared error between the original and the reconstructed label vectors, such that they become more discriminative. Second, Factor analysis (FA) can be performed on the pre-normalized centered GMM first order statistics supervector to ensure that the Gaussian statistics sub-vector of each Gaussian component is treated equally in the FA, which reduces the computational cost significantly. Experimental results are reported on the female part of the NIST SRE 2010 task with common condition 5. The proposed supervised i-vector approach outperforms the i-vector baseline by relatively 12% and 7% in terms of equal error rate (EER) and norm old minDCF values, respectively.
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ICASSP - Speaker verification using simplified and supervised i-vector modeling
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Ming Li, Andreas Tsiartas, Maarten Van Segbroeck, Shrikanth S. NarayananAbstract:This paper presents a simplified and supervised i-vector modeling framework that is applied in the task of robust and efficient speaker verification (SRE). First, by concatenating the mean supervector and the i-vector Factor Loading Matrix with respectively the label vector and the linear classifier Matrix, the traditional i-vectors are then extended to label-regularized supervised i-vectors. These supervised i-vectors are optimized to not only reconstruct the mean supervectors well but also minimize the mean squared error between the original and the reconstructed label vectors, such that they become more discriminative. Second, Factor analysis (FA) can be performed on the pre-normalized centered GMM first order statistics supervector to ensure that the Gaussian statistics sub-vector of each Gaussian component is treated equally in the FA, which reduces the computational cost significantly. Experimental results are reported on the female part of the NIST SRE 2010 task with common condition 5. The proposed supervised i-vector approach outperforms the i-vector baseline by relatively 12% and 7% in terms of equal error rate (EER) and norm old minDCF values, respectively.
Y. Huang - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - Bayesian sparse Factor model for transcriptional regulatory networks inference
2013Co-Authors: M. Sanchez-castillo, I. Tienda-luna, D. Blanco, M. C. Carrion-perez, Y. HuangAbstract:Uncovering transcription Factor (TF) mediated regulatory networks from microarray expression data and prior knowledge is considered in this paper. Bayesian Factor models that model direct TF regulation are formulated. To address the enormous computational complexity of the model in large networks, a novel, efficient basis-expansion Factor model (BEFaM) has been proposed, where the Loading (regulatory) Matrix is modeled as an expansion using basis functions of much lower dimension. Great reduction is achieved with BEFaM as the inference involves estimation of expansion coefficients with much reduced dimensions. We also address the issue of incorporating the prior knowledge of TF regulation to constrain the Factor Loading Matrix. A Gibbs sampling solution has been developed to estimate the unknowns. The proposed model was validated by simulation and then applied to breast cancer data to uncover the corresponding TF regulatory network and theirs protein levels.
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Bayesian sparse Factor model for transcriptional regulatory networks inference
21st European Signal Processing Conference (EUSIPCO 2013), 2013Co-Authors: M. Sanchez-castillo, I. Tienda-luna, D. Blanco, M. C. Carrion-perez, Y. HuangAbstract:Uncovering transcription Factor (TF) mediated regulatory networks from microarray expression data and prior knowledge is considered in this paper. Bayesian Factor models that model direct TF regulation are formulated. To address the enormous computational complexity of the model in large networks, a novel, efficient basis-expansion Factor model (BEFaM) has been proposed, where the Loading (regulatory) Matrix is modeled as an expansion using basis functions of much lower dimension. Great reduction is achieved with BEFaM as the inference involves estimation of expansion coefficients with much reduced dimensions. We also address the issue of incorporating the prior knowledge of TF regulation to constrain the Factor Loading Matrix. A Gibbs sampling solution has been developed to estimate the unknowns. The proposed model was validated by simulation and then applied to breast cancer data to uncover the corresponding TF regulatory network and theirs protein levels.
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SSP - Basis-expansion Factor models for uncovering transcription Factor regulatory network
2012 IEEE Statistical Signal Processing Workshop (SSP), 2012Co-Authors: M. Sanchez-castillo, I. Tienda-luna, Jia Meng, Y. HuangAbstract:Uncovering transcription Factor (TF) mediated regulatory networks from microarray expression data and prior knowledge is considered in this paper. Bayesian Factor models that models direct TF regulation are formulated. To address the enormous computational complexity of the Factor for modeling large networks, a novel, efficient basis-expansion Factor (BE-FaM) model has been proposed, where the Loading (regulatory) Matrix is modeled as an expansion of basis functions of much lower dimension. Great reduction is achieved with BE-FaM as the inference involves instead estimation of expansion coefficients with much reduced dimensions. We also address the issue of incorporating the prior knowledge of TF regulation to constrain the Factor Loading Matrix. A Gibbs sampling solution has been developed to estimate the unknowns. The proposed model was validated by the simulation and then applied to the genomic data of the breast cancer to uncover the corresponding TF regulatory networks.
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Basis-expansion Factor models for uncovering transcription Factor regulatory network
2012 IEEE Statistical Signal Processing Workshop (SSP), 2012Co-Authors: M. Sanchez-castillo, I. Tienda-luna, Jia Meng, Y. HuangAbstract:Uncovering transcription Factor (TF) mediated regulatory networks from microarray expression data and prior knowledge is considered in this paper. Bayesian Factor models that models direct TF regulation are formulated. To address the enormous computational complexity of the Factor for modeling large networks, a novel, efficient basis-expansion Factor (BE-FaM) model has been proposed, where the Loading (regulatory) Matrix is modeled as an expansion of basis functions of much lower dimension. Great reduction is achieved with BE-FaM as the inference involves instead estimation of expansion coefficients with much reduced dimensions. We also address the issue of incorporating the prior knowledge of TF regulation to constrain the Factor Loading Matrix. A Gibbs sampling solution has been developed to estimate the unknowns. The proposed model was validated by the simulation and then applied to the genomic data of the breast cancer to uncover the corresponding TF regulatory networks.
Ming Li - One of the best experts on this subject based on the ideXlab platform.
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simplified supervised i vector modeling with application to robust and efficient language identification and speaker verification
Computer Speech & Language, 2014Co-Authors: Ming Li, Shrikanth S. NarayananAbstract:Abstract This paper presents a simplified and supervised i-vector modeling approach with applications to robust and efficient language identification and speaker verification. First, by concatenating the label vector and the linear regression Matrix at the end of the mean supervector and the i-vector Factor Loading Matrix, respectively, the traditional i-vectors are extended to label-regularized supervised i-vectors. These supervised i-vectors are optimized to not only reconstruct the mean supervectors well but also minimize the mean square error between the original and the reconstructed label vectors to make the supervised i-vectors become more discriminative in terms of the label information. Second, Factor analysis (FA) is performed on the pre-normalized centered GMM first order statistics supervector to ensure each gaussian component's statistics sub-vector is treated equally in the FA, which reduces the computational cost by a Factor of 25 in the simplified i-vector framework. Third, since the entire Matrix inversion term in the simplified i-vector extraction only depends on one single variable (total frame number), we make a global table of the resulting matrices against the frame numbers’ log values. Using this lookup table, each utterance's simplified i-vector extraction is further sped up by a Factor of 4 and suffers only a small quantization error. Finally, the simplified version of the supervised i-vector modeling is proposed to enhance both the robustness and efficiency. The proposed methods are evaluated on the DARPA RATS dev2 task, the NIST LRE 2007 general task and the NIST SRE 2010 female condition 5 task for noisy channel language identification, clean channel language identification and clean channel speaker verification, respectively. For language identification on the DARPA RATS, the simplified supervised i-vector modeling achieved 2%, 16%, and 7% relative equal error rate (EER) reduction on three different feature sets and sped up by a Factor of more than 100 against the baseline i-vector method for the 120 s task. Similar results were observed on the NIST LRE 2007 30 s task with 7% relative average cost reduction. Results also show that the use of Gammatone frequency cepstral coefficients, Mel-frequency cepstral coefficients and spectro-temporal Gabor features in conjunction with shifted-delta-cepstral features improves the overall language identification performance significantly. For speaker verification, the proposed supervised i-vector approach outperforms the i-vector baseline by relatively 12% and 7% in terms of EER and norm old minDCF values, respectively.
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Simplified and supervised i-vector modeling for speaker age regression
2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014Co-Authors: Prashanth Gurunath Shivakumar, Ming Li, Vedant Dhandhania, Shrikanth S. NarayananAbstract:We propose a simplified and supervised i-vector modeling scheme for the speaker age regression task. The supervised i-vector is obtained by concatenating the label vector and the linear regression Matrix at the end of the mean super-vector and the i-vector Factor Loading Matrix, respectively. Different label vector designs are proposed to increase the robustness of the supervised i-vector models. Finally, Support Vector Regression (SVR) is deployed to estimate the age of the speakers. The proposed method outperforms the conventional i-vector baseline for speaker age estimation. A relative 2.4% decrease in Mean Absolute Error and 3.33% increase in correlation coefficient is achieved using supervised i-vector modeling using different label designs on the NIST SRE 2008 dataset male part.
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ICASSP - SIMPLIFIED AND SUPERVISED I-VECTOR MODELING FOR SPEAKER AGE REGRESSION
2014 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2014Co-Authors: Prashanth Gurunath Shivakumar, Ming Li, Vedant Dhandhania, Shrikanth S. NarayananAbstract:We propose a simplified and supervised i-vector modeling scheme for the speaker age regression task. The supervised i-vector is obtained by concatenating the label vector and the linear regression Matrix at the end of the mean super-vector and the i-vector Factor Loading Matrix, respectively. Different label vector designs are proposed to increase the robustness of the supervised i-vector models. Finally, Support Vector Regression (SVR) is deployed to estimate the age of the speakers. The proposed method outperforms the conventional i-vector baseline for speaker age estimation. A relative 2.4% decrease in Mean Absolute Error and 3.33% increase in correlation coefficient is achieved using supervised i-vector modeling using different label designs on the NIST SRE 2008 dataset male part.
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Speaker verification using simplified and supervised i-vector modeling
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Ming Li, Andreas Tsiartas, Maarten Van Segbroeck, Shrikanth S. NarayananAbstract:This paper presents a simplified and supervised i-vector modeling framework that is applied in the task of robust and efficient speaker verification (SRE). First, by concatenating the mean supervector and the i-vector Factor Loading Matrix with respectively the label vector and the linear classifier Matrix, the traditional i-vectors are then extended to label-regularized supervised i-vectors. These supervised i-vectors are optimized to not only reconstruct the mean supervectors well but also minimize the mean squared error between the original and the reconstructed label vectors, such that they become more discriminative. Second, Factor analysis (FA) can be performed on the pre-normalized centered GMM first order statistics supervector to ensure that the Gaussian statistics sub-vector of each Gaussian component is treated equally in the FA, which reduces the computational cost significantly. Experimental results are reported on the female part of the NIST SRE 2010 task with common condition 5. The proposed supervised i-vector approach outperforms the i-vector baseline by relatively 12% and 7% in terms of equal error rate (EER) and norm old minDCF values, respectively.
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ICASSP - Speaker verification using simplified and supervised i-vector modeling
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Ming Li, Andreas Tsiartas, Maarten Van Segbroeck, Shrikanth S. NarayananAbstract:This paper presents a simplified and supervised i-vector modeling framework that is applied in the task of robust and efficient speaker verification (SRE). First, by concatenating the mean supervector and the i-vector Factor Loading Matrix with respectively the label vector and the linear classifier Matrix, the traditional i-vectors are then extended to label-regularized supervised i-vectors. These supervised i-vectors are optimized to not only reconstruct the mean supervectors well but also minimize the mean squared error between the original and the reconstructed label vectors, such that they become more discriminative. Second, Factor analysis (FA) can be performed on the pre-normalized centered GMM first order statistics supervector to ensure that the Gaussian statistics sub-vector of each Gaussian component is treated equally in the FA, which reduces the computational cost significantly. Experimental results are reported on the female part of the NIST SRE 2010 task with common condition 5. The proposed supervised i-vector approach outperforms the i-vector baseline by relatively 12% and 7% in terms of equal error rate (EER) and norm old minDCF values, respectively.
M. Sanchez-castillo - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - Bayesian sparse Factor model for transcriptional regulatory networks inference
2013Co-Authors: M. Sanchez-castillo, I. Tienda-luna, D. Blanco, M. C. Carrion-perez, Y. HuangAbstract:Uncovering transcription Factor (TF) mediated regulatory networks from microarray expression data and prior knowledge is considered in this paper. Bayesian Factor models that model direct TF regulation are formulated. To address the enormous computational complexity of the model in large networks, a novel, efficient basis-expansion Factor model (BEFaM) has been proposed, where the Loading (regulatory) Matrix is modeled as an expansion using basis functions of much lower dimension. Great reduction is achieved with BEFaM as the inference involves estimation of expansion coefficients with much reduced dimensions. We also address the issue of incorporating the prior knowledge of TF regulation to constrain the Factor Loading Matrix. A Gibbs sampling solution has been developed to estimate the unknowns. The proposed model was validated by simulation and then applied to breast cancer data to uncover the corresponding TF regulatory network and theirs protein levels.
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Bayesian sparse Factor model for transcriptional regulatory networks inference
21st European Signal Processing Conference (EUSIPCO 2013), 2013Co-Authors: M. Sanchez-castillo, I. Tienda-luna, D. Blanco, M. C. Carrion-perez, Y. HuangAbstract:Uncovering transcription Factor (TF) mediated regulatory networks from microarray expression data and prior knowledge is considered in this paper. Bayesian Factor models that model direct TF regulation are formulated. To address the enormous computational complexity of the model in large networks, a novel, efficient basis-expansion Factor model (BEFaM) has been proposed, where the Loading (regulatory) Matrix is modeled as an expansion using basis functions of much lower dimension. Great reduction is achieved with BEFaM as the inference involves estimation of expansion coefficients with much reduced dimensions. We also address the issue of incorporating the prior knowledge of TF regulation to constrain the Factor Loading Matrix. A Gibbs sampling solution has been developed to estimate the unknowns. The proposed model was validated by simulation and then applied to breast cancer data to uncover the corresponding TF regulatory network and theirs protein levels.
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SSP - Basis-expansion Factor models for uncovering transcription Factor regulatory network
2012 IEEE Statistical Signal Processing Workshop (SSP), 2012Co-Authors: M. Sanchez-castillo, I. Tienda-luna, Jia Meng, Y. HuangAbstract:Uncovering transcription Factor (TF) mediated regulatory networks from microarray expression data and prior knowledge is considered in this paper. Bayesian Factor models that models direct TF regulation are formulated. To address the enormous computational complexity of the Factor for modeling large networks, a novel, efficient basis-expansion Factor (BE-FaM) model has been proposed, where the Loading (regulatory) Matrix is modeled as an expansion of basis functions of much lower dimension. Great reduction is achieved with BE-FaM as the inference involves instead estimation of expansion coefficients with much reduced dimensions. We also address the issue of incorporating the prior knowledge of TF regulation to constrain the Factor Loading Matrix. A Gibbs sampling solution has been developed to estimate the unknowns. The proposed model was validated by the simulation and then applied to the genomic data of the breast cancer to uncover the corresponding TF regulatory networks.
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Basis-expansion Factor models for uncovering transcription Factor regulatory network
2012 IEEE Statistical Signal Processing Workshop (SSP), 2012Co-Authors: M. Sanchez-castillo, I. Tienda-luna, Jia Meng, Y. HuangAbstract:Uncovering transcription Factor (TF) mediated regulatory networks from microarray expression data and prior knowledge is considered in this paper. Bayesian Factor models that models direct TF regulation are formulated. To address the enormous computational complexity of the Factor for modeling large networks, a novel, efficient basis-expansion Factor (BE-FaM) model has been proposed, where the Loading (regulatory) Matrix is modeled as an expansion of basis functions of much lower dimension. Great reduction is achieved with BE-FaM as the inference involves instead estimation of expansion coefficients with much reduced dimensions. We also address the issue of incorporating the prior knowledge of TF regulation to constrain the Factor Loading Matrix. A Gibbs sampling solution has been developed to estimate the unknowns. The proposed model was validated by the simulation and then applied to the genomic data of the breast cancer to uncover the corresponding TF regulatory networks.
I. Tienda-luna - One of the best experts on this subject based on the ideXlab platform.
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EUSIPCO - Bayesian sparse Factor model for transcriptional regulatory networks inference
2013Co-Authors: M. Sanchez-castillo, I. Tienda-luna, D. Blanco, M. C. Carrion-perez, Y. HuangAbstract:Uncovering transcription Factor (TF) mediated regulatory networks from microarray expression data and prior knowledge is considered in this paper. Bayesian Factor models that model direct TF regulation are formulated. To address the enormous computational complexity of the model in large networks, a novel, efficient basis-expansion Factor model (BEFaM) has been proposed, where the Loading (regulatory) Matrix is modeled as an expansion using basis functions of much lower dimension. Great reduction is achieved with BEFaM as the inference involves estimation of expansion coefficients with much reduced dimensions. We also address the issue of incorporating the prior knowledge of TF regulation to constrain the Factor Loading Matrix. A Gibbs sampling solution has been developed to estimate the unknowns. The proposed model was validated by simulation and then applied to breast cancer data to uncover the corresponding TF regulatory network and theirs protein levels.
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Bayesian sparse Factor model for transcriptional regulatory networks inference
21st European Signal Processing Conference (EUSIPCO 2013), 2013Co-Authors: M. Sanchez-castillo, I. Tienda-luna, D. Blanco, M. C. Carrion-perez, Y. HuangAbstract:Uncovering transcription Factor (TF) mediated regulatory networks from microarray expression data and prior knowledge is considered in this paper. Bayesian Factor models that model direct TF regulation are formulated. To address the enormous computational complexity of the model in large networks, a novel, efficient basis-expansion Factor model (BEFaM) has been proposed, where the Loading (regulatory) Matrix is modeled as an expansion using basis functions of much lower dimension. Great reduction is achieved with BEFaM as the inference involves estimation of expansion coefficients with much reduced dimensions. We also address the issue of incorporating the prior knowledge of TF regulation to constrain the Factor Loading Matrix. A Gibbs sampling solution has been developed to estimate the unknowns. The proposed model was validated by simulation and then applied to breast cancer data to uncover the corresponding TF regulatory network and theirs protein levels.
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SSP - Basis-expansion Factor models for uncovering transcription Factor regulatory network
2012 IEEE Statistical Signal Processing Workshop (SSP), 2012Co-Authors: M. Sanchez-castillo, I. Tienda-luna, Jia Meng, Y. HuangAbstract:Uncovering transcription Factor (TF) mediated regulatory networks from microarray expression data and prior knowledge is considered in this paper. Bayesian Factor models that models direct TF regulation are formulated. To address the enormous computational complexity of the Factor for modeling large networks, a novel, efficient basis-expansion Factor (BE-FaM) model has been proposed, where the Loading (regulatory) Matrix is modeled as an expansion of basis functions of much lower dimension. Great reduction is achieved with BE-FaM as the inference involves instead estimation of expansion coefficients with much reduced dimensions. We also address the issue of incorporating the prior knowledge of TF regulation to constrain the Factor Loading Matrix. A Gibbs sampling solution has been developed to estimate the unknowns. The proposed model was validated by the simulation and then applied to the genomic data of the breast cancer to uncover the corresponding TF regulatory networks.
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Basis-expansion Factor models for uncovering transcription Factor regulatory network
2012 IEEE Statistical Signal Processing Workshop (SSP), 2012Co-Authors: M. Sanchez-castillo, I. Tienda-luna, Jia Meng, Y. HuangAbstract:Uncovering transcription Factor (TF) mediated regulatory networks from microarray expression data and prior knowledge is considered in this paper. Bayesian Factor models that models direct TF regulation are formulated. To address the enormous computational complexity of the Factor for modeling large networks, a novel, efficient basis-expansion Factor (BE-FaM) model has been proposed, where the Loading (regulatory) Matrix is modeled as an expansion of basis functions of much lower dimension. Great reduction is achieved with BE-FaM as the inference involves instead estimation of expansion coefficients with much reduced dimensions. We also address the issue of incorporating the prior knowledge of TF regulation to constrain the Factor Loading Matrix. A Gibbs sampling solution has been developed to estimate the unknowns. The proposed model was validated by the simulation and then applied to the genomic data of the breast cancer to uncover the corresponding TF regulatory networks.
