Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081407
Xiaoguang Wu, Huawei Chen
Second-order differential microphone arrays (DMAs) are one of the most commonly used DMAs in practice due to the sensitivity of higher-order DMAs to microphone mis-matches and self-noise. However, conventional second-order DMAs are non-steerable with their mainlobe orientation fixed along the array end fire direction, which are not applicable to the case where sound sources may move around a large angular range. In this paper, we propose a design of second-order steerable DMAs (SOSDAs) using seven microphones. The design procedure is discussed, followed by the theoretical analysis on directivity factor and white noise gain of the proposed SOSDAs. Numerical examples are shown to demonstrate the effectiveness of the proposed design and its theoretical analysis.
{"title":"Design and analysis of second-order steerable differential microphone arrays","authors":"Xiaoguang Wu, Huawei Chen","doi":"10.23919/EUSIPCO.2017.8081407","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081407","url":null,"abstract":"Second-order differential microphone arrays (DMAs) are one of the most commonly used DMAs in practice due to the sensitivity of higher-order DMAs to microphone mis-matches and self-noise. However, conventional second-order DMAs are non-steerable with their mainlobe orientation fixed along the array end fire direction, which are not applicable to the case where sound sources may move around a large angular range. In this paper, we propose a design of second-order steerable DMAs (SOSDAs) using seven microphones. The design procedure is discussed, followed by the theoretical analysis on directivity factor and white noise gain of the proposed SOSDAs. Numerical examples are shown to demonstrate the effectiveness of the proposed design and its theoretical analysis.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115121500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081447
A. Panousopoulou, S. Farrens, Yiannis Mastorakis, Jean-Luc Starck, P. Tsakalides
Future challenges in Big Imaging problems will require that traditional, "black-box" machine learning methods, be revisited from the perspective of ongoing efforts in distributed computing. This paper proposes a distributed architecture for astrophysical imagery, which exploits the Apache Spark framework for the efficient parallelization of the learning problem at hand. The use case is related to the challenging problem of deconvolving a space variant point spread function from noisy galaxy images. We conduct benchmark studies considering relevant datasets and analyze the efficacy of the herein developed parallelization approaches. The experimental results report 58% improvement in time response terms against the conventional computing solutions, while useful insights into the computational trade-offs and the limitations of Spark are extracted.
{"title":"A distributed learning architecture for big imaging problems in astrophysics","authors":"A. Panousopoulou, S. Farrens, Yiannis Mastorakis, Jean-Luc Starck, P. Tsakalides","doi":"10.23919/EUSIPCO.2017.8081447","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081447","url":null,"abstract":"Future challenges in Big Imaging problems will require that traditional, \"black-box\" machine learning methods, be revisited from the perspective of ongoing efforts in distributed computing. This paper proposes a distributed architecture for astrophysical imagery, which exploits the Apache Spark framework for the efficient parallelization of the learning problem at hand. The use case is related to the challenging problem of deconvolving a space variant point spread function from noisy galaxy images. We conduct benchmark studies considering relevant datasets and analyze the efficacy of the herein developed parallelization approaches. The experimental results report 58% improvement in time response terms against the conventional computing solutions, while useful insights into the computational trade-offs and the limitations of Spark are extracted.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115130830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081388
F. Nesta, Saeed Mosayyebpour, Zbyněk Koldovský, K. Paleček
Independent Vector Analysis is a powerful tool for estimating the broadband acoustic transfer function between multiple sources and the microphones in the frequency domain. In this work, we consider an extended IVA model which adopts the concept of pilot dependent signals. Without imposing any constraint on the de-mixing system, pilot signals depending on the target source are injected into the model enforcing the permutation of outputs to be consistent over time. A neural network trained on acoustic data and a lip motion detection are jointly used to produce a multimodal pilot signal dependent on the target source. It is shown through experimental results that this structure allows the enhancement of a predefined target source in very difficult and ambiguous scenarios.
{"title":"Audio/video supervised independent vector analysis through multimodal pilot dependent components","authors":"F. Nesta, Saeed Mosayyebpour, Zbyněk Koldovský, K. Paleček","doi":"10.23919/EUSIPCO.2017.8081388","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081388","url":null,"abstract":"Independent Vector Analysis is a powerful tool for estimating the broadband acoustic transfer function between multiple sources and the microphones in the frequency domain. In this work, we consider an extended IVA model which adopts the concept of pilot dependent signals. Without imposing any constraint on the de-mixing system, pilot signals depending on the target source are injected into the model enforcing the permutation of outputs to be consistent over time. A neural network trained on acoustic data and a lip motion detection are jointly used to produce a multimodal pilot signal dependent on the target source. It is shown through experimental results that this structure allows the enhancement of a predefined target source in very difficult and ambiguous scenarios.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117160985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081204
Yanyan Wang, Yingsong Li, Rui Yang
A sparsity-aware proportionate normalized maximum correntropy criterion (PNMCC) algorithm with lp-norm penalty, which is named as lp-norm constraint PNMCC (LP-PNMCC), is proposed and its crucial parameters, convergence speed rate and steady-state performance are discussed via estimating a typical sparse multipath channel and an typical echo channel. The LP-PNMCC algorithm is realized by integrating a lp-norm into the PNMCC's cost function to create an expected zero attraction term in the iterations of the presented LP-PNMCC algorithm, which aims to further exploit the sparsity property of the sparse channels. The presented LP-PNMCC algorithm has been derived and analyzed in detail. Experimental results obtained from sparse channel estimations demonstrate that the proposed LP-PNMCC algorithm is superior to the PNMCC, PNLMS, RZA-MCC, ZA-MCC, NMCC and MCC algorithms according to the convergence speed rate and steady-state mean square deviation.
{"title":"A sparsity-aware proportionate normalized maximum correntropy criterion algorithm for sparse system identification in non-Gaussian environment","authors":"Yanyan Wang, Yingsong Li, Rui Yang","doi":"10.23919/EUSIPCO.2017.8081204","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081204","url":null,"abstract":"A sparsity-aware proportionate normalized maximum correntropy criterion (PNMCC) algorithm with lp-norm penalty, which is named as lp-norm constraint PNMCC (LP-PNMCC), is proposed and its crucial parameters, convergence speed rate and steady-state performance are discussed via estimating a typical sparse multipath channel and an typical echo channel. The LP-PNMCC algorithm is realized by integrating a lp-norm into the PNMCC's cost function to create an expected zero attraction term in the iterations of the presented LP-PNMCC algorithm, which aims to further exploit the sparsity property of the sparse channels. The presented LP-PNMCC algorithm has been derived and analyzed in detail. Experimental results obtained from sparse channel estimations demonstrate that the proposed LP-PNMCC algorithm is superior to the PNMCC, PNLMS, RZA-MCC, ZA-MCC, NMCC and MCC algorithms according to the convergence speed rate and steady-state mean square deviation.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125481417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081220
Stanley Smith, M. Pischella, M. Terré
We present a density-based clustering method producing a covering of the dataset by ellipsoidal structures in order to detect possibly entangled clusters. We first introduce an unconstrained version of the algorithm which does not require any assumption on the number of clusters. Then a constrained version using a priori knowledge to improve the bare clustering is discussed. We evaluate the performance of our algorithm and several other well-known clustering methods using existing cluster validity techniques on randomly-generated bi-dimensional gaussian mixtures. Our simulation results show that both versions of our algorithm compare well with the reference algorithms according to the used metrics, foreseeing future improvements of our method.
{"title":"An elliptical-shaped density-based classification algorithm for detection of entangled clusters","authors":"Stanley Smith, M. Pischella, M. Terré","doi":"10.23919/EUSIPCO.2017.8081220","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081220","url":null,"abstract":"We present a density-based clustering method producing a covering of the dataset by ellipsoidal structures in order to detect possibly entangled clusters. We first introduce an unconstrained version of the algorithm which does not require any assumption on the number of clusters. Then a constrained version using a priori knowledge to improve the bare clustering is discussed. We evaluate the performance of our algorithm and several other well-known clustering methods using existing cluster validity techniques on randomly-generated bi-dimensional gaussian mixtures. Our simulation results show that both versions of our algorithm compare well with the reference algorithms according to the used metrics, foreseeing future improvements of our method.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115024324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081572
K. Todros
In this paper, we develop a robust generalization of the Gaussian quasi score test (GQST) for composite binary hypothesis testing. The proposed test, called measure-transformed GQST (MT-GQST), is based on a transformation applied to the probability distribution of the data. The considered transform is structured by a non-negative function, called MT-function, that weights the data points. By appropriate selection of the MT-function we show that, unlike the GQST, the proposed MT-GQST incorporates higher-order moments and can gain robustness to outliers. The MT-GQST is applied for testing the parameter of a non-linear model. Simulation example illustrates its advantages as compared to the standard GQST and other robust detectors.
{"title":"Measure-transformed Gaussian quasi score test","authors":"K. Todros","doi":"10.23919/EUSIPCO.2017.8081572","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081572","url":null,"abstract":"In this paper, we develop a robust generalization of the Gaussian quasi score test (GQST) for composite binary hypothesis testing. The proposed test, called measure-transformed GQST (MT-GQST), is based on a transformation applied to the probability distribution of the data. The considered transform is structured by a non-negative function, called MT-function, that weights the data points. By appropriate selection of the MT-function we show that, unlike the GQST, the proposed MT-GQST incorporates higher-order moments and can gain robustness to outliers. The MT-GQST is applied for testing the parameter of a non-linear model. Simulation example illustrates its advantages as compared to the standard GQST and other robust detectors.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115123068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081521
Dharmesh M. Agrawal, Hardik B. Sailor, Meet H. Soni, H. Patil
In this paper, we propose to use modified Gammatone filterbank with Teager Energy Operator (TEO) for environmental sound classification (ESC) task. TEO can track energy as a function of both amplitude and frequency of an audio signal. TEO is better for capturing energy variations in the signal that is produced by a real physical system, such as, environmental sounds that contain amplitude and frequency modulations. In proposed feature set, we have used Gammatone filterbank since it represents characteristics of human auditory processing. Here, we have used two classifiers, namely, Gaussian Mixture Model (GMM) using cepstral features, and Convolutional Neural Network (CNN) using spectral features. We performed experiments on two datasets, namely, ESC-50, and UrbanSound8K. We compared TEO-based coefficients with Mel filter cepstral coefficients (MFCC) and Gammatone cepstral coefficients (GTCC), in which GTCC used mean square energy. Using GMM, the proposed TEO-based Gammatone Cepstral Coefficients (TEO-GTCC), and its score-level fusion with MFCC gave absolute improvement of 0.45 %, and 3.85 % in classification accuracy over MFCC on ESC-50 dataset. Similarly, on UrbanSound8K dataset the proposed TEO-GTCC, and its score-level fusion with GTCC gave absolute improvement of 1.40 %, and 2.44 % in classification accuracy over MFCC. Using CNN, the score-level fusion of Gammatone spectral coefficient (GTSC) and the proposed TEO-based Gammatone spectral coefficients (TEO-GTSC) gave absolute improvement of 14.10 %, and 14.52 % in classification accuracy over Mel filterbank energies (FBE) on ESC-50 and UrbanSond8K datasets, respectively. This shows that proposed TEO-based Gammatone features contain complementary information which is helpful in ESC task.
{"title":"Novel TEO-based Gammatone features for environmental sound classification","authors":"Dharmesh M. Agrawal, Hardik B. Sailor, Meet H. Soni, H. Patil","doi":"10.23919/EUSIPCO.2017.8081521","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081521","url":null,"abstract":"In this paper, we propose to use modified Gammatone filterbank with Teager Energy Operator (TEO) for environmental sound classification (ESC) task. TEO can track energy as a function of both amplitude and frequency of an audio signal. TEO is better for capturing energy variations in the signal that is produced by a real physical system, such as, environmental sounds that contain amplitude and frequency modulations. In proposed feature set, we have used Gammatone filterbank since it represents characteristics of human auditory processing. Here, we have used two classifiers, namely, Gaussian Mixture Model (GMM) using cepstral features, and Convolutional Neural Network (CNN) using spectral features. We performed experiments on two datasets, namely, ESC-50, and UrbanSound8K. We compared TEO-based coefficients with Mel filter cepstral coefficients (MFCC) and Gammatone cepstral coefficients (GTCC), in which GTCC used mean square energy. Using GMM, the proposed TEO-based Gammatone Cepstral Coefficients (TEO-GTCC), and its score-level fusion with MFCC gave absolute improvement of 0.45 %, and 3.85 % in classification accuracy over MFCC on ESC-50 dataset. Similarly, on UrbanSound8K dataset the proposed TEO-GTCC, and its score-level fusion with GTCC gave absolute improvement of 1.40 %, and 2.44 % in classification accuracy over MFCC. Using CNN, the score-level fusion of Gammatone spectral coefficient (GTSC) and the proposed TEO-based Gammatone spectral coefficients (TEO-GTSC) gave absolute improvement of 14.10 %, and 14.52 % in classification accuracy over Mel filterbank energies (FBE) on ESC-50 and UrbanSond8K datasets, respectively. This shows that proposed TEO-based Gammatone features contain complementary information which is helpful in ESC task.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115196883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081418
Michel Haritopoulos, J. Krug, A. Illanes, M. Friebe, A. Nandi
In this paper, a strategy is proposed to estimate the R-peaks in ECG signals recorded inside a 7 T magnetic resonance imaging (MRI) scanner in order to reduce the disturbances due to the magnetohydrodynamic (MHD) effect and to finally obtain high quality cardiovascular magnetic resonance (CMR) images. We first show that the cyclostationarity property of the ECG signal disturbed by the MHD effect can be quantified by means of cyclic spectral analysis. Then, this information is forwarded as input to a cyclostationary source extraction algorithm applied to a set of ECG recordings acquired inside the MRI scanner in a Feet first (Ff) and a Head first (Hf) positions. Finally, detection of the R-peaks in the estimated cyclostationary signal completes the proposed procedure. Validation of the method is performed by comparing the estimated with clinical R-peaks annotations provided with the real world dataset. The obtained results are promising and future research directions are discussed.
{"title":"Cyclostationary analysis of ECG signals acquired inside an ultra-high field MRI scanner","authors":"Michel Haritopoulos, J. Krug, A. Illanes, M. Friebe, A. Nandi","doi":"10.23919/EUSIPCO.2017.8081418","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081418","url":null,"abstract":"In this paper, a strategy is proposed to estimate the R-peaks in ECG signals recorded inside a 7 T magnetic resonance imaging (MRI) scanner in order to reduce the disturbances due to the magnetohydrodynamic (MHD) effect and to finally obtain high quality cardiovascular magnetic resonance (CMR) images. We first show that the cyclostationarity property of the ECG signal disturbed by the MHD effect can be quantified by means of cyclic spectral analysis. Then, this information is forwarded as input to a cyclostationary source extraction algorithm applied to a set of ECG recordings acquired inside the MRI scanner in a Feet first (Ff) and a Head first (Hf) positions. Finally, detection of the R-peaks in the estimated cyclostationary signal completes the proposed procedure. Validation of the method is performed by comparing the estimated with clinical R-peaks annotations provided with the real world dataset. The obtained results are promising and future research directions are discussed.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115342108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081466
T. Strutz, Alexander Leipnitz
Recent developments in the standardisation of High Efficiency Video Coding (HEVC) have shown that the block-wise activation/deactivation of a colour transform can significantly improve the compression performance. This coding tool is based on a fixed colour space which is either YCgCo in lossy compression mode or YCgCo-R in the lossless mode. The proposed method shows that the performance can be increased even more when the colour space is not fixed but selected dependent on the image characteristic. Improvements of more than 2% can be achieved in lossless intra coding if the colour space is automatically chosen once for the entire image. In lossy intra compression, the performance can also be increased if a proper colour space is chosen.
{"title":"Adaptive colour-space selection in high efficiency video coding","authors":"T. Strutz, Alexander Leipnitz","doi":"10.23919/EUSIPCO.2017.8081466","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081466","url":null,"abstract":"Recent developments in the standardisation of High Efficiency Video Coding (HEVC) have shown that the block-wise activation/deactivation of a colour transform can significantly improve the compression performance. This coding tool is based on a fixed colour space which is either YCgCo in lossy compression mode or YCgCo-R in the lossless mode. The proposed method shows that the performance can be increased even more when the colour space is not fixed but selected dependent on the image characteristic. Improvements of more than 2% can be achieved in lossless intra coding if the colour space is automatically chosen once for the entire image. In lossy intra compression, the performance can also be increased if a proper colour space is chosen.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122412492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081535
F. Nikolay, M. Pesavento
In this paper we consider the problem of learning the topology of a directed-acyclic-graph, that describes the interactions among a set of genes, based on noisy double knockout data and genetic-interactions-profile data. We propose a novel linear integer optimization approach to identify the complex biological dependencies among genes and to compute the topology of the directed-acyclic-graph that matches the data best. Finally, we apply a sequential scalability technique for large sets of genes along with our proposed algorithm, in order to provide statistically significant results for experimental data.
{"title":"Learning directed-acyclic-graphs from multiple genomic data sources","authors":"F. Nikolay, M. Pesavento","doi":"10.23919/EUSIPCO.2017.8081535","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081535","url":null,"abstract":"In this paper we consider the problem of learning the topology of a directed-acyclic-graph, that describes the interactions among a set of genes, based on noisy double knockout data and genetic-interactions-profile data. We propose a novel linear integer optimization approach to identify the complex biological dependencies among genes and to compute the topology of the directed-acyclic-graph that matches the data best. Finally, we apply a sequential scalability technique for large sets of genes along with our proposed algorithm, in order to provide statistically significant results for experimental data.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122486601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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