The Experts below are selected from a list of 47283 Experts worldwide ranked by ideXlab platform
Ling Guan - One of the best experts on this subject based on the ideXlab platform.
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Discriminative Multiple Canonical Correlation Analysis for Information Fusion
2018Co-Authors: Lin Qi, Enqing Chen, Ling GuanAbstract:In this paper, we propose the discriminative multiple canonical correlation analysis (DMCCA) for Multimodal Information analysis and fusion. DMCCA is capable of extracting more discriminative characteristics from Multimodal Information representations. Specifically, it finds the projected directions, which simultaneously maximize the within-class correlation and minimize the between-class correlation, leading to better utilization of the Multimodal Information. In the process, we analytically demonstrate that the optimally projected dimension by DMCCA can be quite accurately predicted, leading to both superior performance and substantial reduction in computational cost. We further verify that canonical correlation analysis (CCA), multiple canonical correlation analysis (MCCA) and discriminative canonical correlation analysis (DCCA) are special cases of DMCCA, thus establishing a unified framework for canonical correlation analysis. We implement a prototype of DMCCA to demonstrate its performance in handwritten digit recognition and human emotion recognition. Extensive experiments show that DMCCA outperforms the traditional methods of serial fusion, CCA, MCCA, and DCCA.
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Multimodal Information fusion of audiovisual emotion recognition using novel Information theoretic tools
2013Co-Authors: Zhibing Xie, Ling GuanAbstract:This paper aims at providing general theoretical analysis for the issue of Multimodal Information fusion and implementing novel Information theoretic tools in multimedia application. The most essential issues for Information fusion include feature transformation and reduction of feature dimensionality. Most previous solutions are largely based on the second order statistics, which is only optimal for Gaussian-like distribution, while in this paper we describe kernel entropy component analysis KECA which utilizes descriptor of Information entropy and achieves improved performance by entropy estimation. The authors present a new solution based on the integration of Information fusion theory and Information theoretic tools in this paper. The proposed method has been applied to audiovisual emotion recognition. Information fusion has been implemented for audio and video channels at feature level and decision level. Experimental results demonstrate that the proposed algorithm achieves improved performance in comparison with the existing methods, especially when the dimension of feature space is substantially reduced.
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Multimodal Information fusion of audio emotion recognition based on kernel entropy component analysis
2013Co-Authors: Zhibing Xie, Ling GuanAbstract:This paper focuses on the application of novel Information theoretic tools in the area of Information fusion. Feature transformation and fusion is critical for the performance of Information fusion, however, the majority of the existing works depend on second order statistics, which is only optimal for Gaussian-like distribution. In this paper, the integration of Information fusion techniques and kernel entropy component analysis provides a new Information theoretic tool. The fusion of features is realized using descriptor of Information entropy and is optimized by entropy estimation. A novel Multimodal Information fusion strategy of audio emotion recognition based on kernel entropy component analysis (KECA) has been presented. The effectiveness of the proposed solution is evaluated through experimentation on two audiovisual emotion databases. Experimental results show that the proposed solution outperforms the existing methods, especially when the dimension of feature space is substantially reduced. The proposed method offers general theoretical analysis which gives us an approach to implement Information theory into multimedia research.
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Multimodal Information fusion of audiovisual emotion recognition using novel Information theoretic tools
2013Co-Authors: Zhibing Xie, Ling GuanAbstract:This paper aims at providing general theoretical analysis for the issue of Multimodal Information fusion and implementing novel Information theoretic tools in multimedia application. The most essential issues for Information fusion include feature transformation and reduction of feature dimensionality. Most previous solutions are based on the second order statistics, which is only optimal for Gaussian-like distribution, while in this paper we describe kernel entropy component analysis (KECA) which utilizes descriptor of Information entropy and achieves improved performance by entropy estimation. We present a new solution based on the integration of Information fusion theory and Information theoretic tools in this paper. The proposed method has been applied to audiovisual emotion recognition. Information fusion has been implemented for audio and video channels at feature level and decision level. Experimental results demonstrate that the proposed algorithm achieves improved performance in comparison with the existing methods, especially when the dimension of feature space is substantially reduced.
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Multimodal Information fusion of audio emotion recognition based on kernel entropy component analysis
2012Co-Authors: Zhibing Xie, Ling GuanAbstract:This paper focuses on the application of novel Information theoretic tools in the area of Information fusion. Feature transformation and fusion is critical for the performance of Information fusion, however the majority of the existing works depend on the second order statistics, which is only optimal for Gaussian-like distribution. In this paper, the integration of Information fusion techniques and kernel entropy component analysis provides a new Information theoretic tool. The fusion of features is realized using descriptor of Information entropy and optimized by entropy estimation. A novel Multimodal Information fusion strategy of audio emotion recognition based on kernel entropy component analysis (KECA) has been presented. The effectiveness of the proposed solution is evaluated though experimentation on two audiovisual emotion databases. Experimental results show that the proposed solution outperforms the existing methods, especially when the dimension of feature space is substantially reduced. The proposed method offers general theoretical analysis which gives us an approach to implement Information theory into multimedia research.
Vince D Calhoun - One of the best experts on this subject based on the ideXlab platform.
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multidataset independent subspace analysis with application to Multimodal fusion
2021Co-Authors: Rogers F Silva, Sergey M Plis, Tulay Adali, Marios S Pattichis, Vince D CalhounAbstract:Unsupervised latent variable models—blind source separation (BSS) especially—enjoy a strong reputation for their interpretability. But they seldom combine the rich diversity of Information available in multiple datasets, even though multidatasets yield insightful joint solutions otherwise unavailable in isolation. We present a direct, principled approach to multidataset combination that takes advantage of multidimensional subspace structures. In turn, we extend BSS models to capture the underlying modes of shared and unique variability across and within datasets. Our approach leverages joint Information from heterogeneous datasets in a flexible and synergistic fashion. We call this method multidataset independent subspace analysis (MISA). Methodological innovations exploiting the Kotz distribution for subspace modeling, in conjunction with a novel combinatorial optimization for evasion of local minima, enable MISA to produce a robust generalization of independent component analysis (ICA), independent vector analysis (IVA), and independent subspace analysis (ISA) in a single unified model. We highlight the utility of MISA for Multimodal Information fusion, including sample-poor regimes ( $N = 600$ ) and low signal-to-noise ratio, promoting novel applications in both unimodal and Multimodal brain imaging data.
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multidataset independent subspace analysis with application to Multimodal fusion
2019Co-Authors: Rogers F Silva, Sergey M Plis, Tulay Adali, Marios S Pattichis, Vince D CalhounAbstract:In the last two decades, unsupervised latent variable models---blind source separation (BSS) especially---have enjoyed a strong reputation for the interpretable features they produce. Seldom do these models combine the rich diversity of Information available in multiple datasets. Multidatasets, on the other hand, yield joint solutions otherwise unavailable in isolation, with a potential for pivotal insights into complex systems. To take advantage of the complex multidimensional subspace structures that capture underlying modes of shared and unique variability across and within datasets, we present a direct, principled approach to multidataset combination. We design a new method called multidataset independent subspace analysis (MISA) that leverages joint Information from multiple heterogeneous datasets in a flexible and synergistic fashion. Methodological innovations exploiting the Kotz distribution for subspace modeling in conjunction with a novel combinatorial optimization for evasion of local minima enable MISA to produce a robust generalization of independent component analysis (ICA), independent vector analysis (IVA), and independent subspace analysis (ISA) in a single unified model. We highlight the utility of MISA for Multimodal Information fusion, including sample-poor regimes and low signal-to-noise ratio scenarios, promoting novel applications in both unimodal and Multimodal brain imaging data.
Zhibing Xie - One of the best experts on this subject based on the ideXlab platform.
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Multimodal Information fusion of audiovisual emotion recognition using novel Information theoretic tools
2013Co-Authors: Zhibing Xie, Ling GuanAbstract:This paper aims at providing general theoretical analysis for the issue of Multimodal Information fusion and implementing novel Information theoretic tools in multimedia application. The most essential issues for Information fusion include feature transformation and reduction of feature dimensionality. Most previous solutions are largely based on the second order statistics, which is only optimal for Gaussian-like distribution, while in this paper we describe kernel entropy component analysis KECA which utilizes descriptor of Information entropy and achieves improved performance by entropy estimation. The authors present a new solution based on the integration of Information fusion theory and Information theoretic tools in this paper. The proposed method has been applied to audiovisual emotion recognition. Information fusion has been implemented for audio and video channels at feature level and decision level. Experimental results demonstrate that the proposed algorithm achieves improved performance in comparison with the existing methods, especially when the dimension of feature space is substantially reduced.
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Multimodal Information fusion of audio emotion recognition based on kernel entropy component analysis
2013Co-Authors: Zhibing Xie, Ling GuanAbstract:This paper focuses on the application of novel Information theoretic tools in the area of Information fusion. Feature transformation and fusion is critical for the performance of Information fusion, however, the majority of the existing works depend on second order statistics, which is only optimal for Gaussian-like distribution. In this paper, the integration of Information fusion techniques and kernel entropy component analysis provides a new Information theoretic tool. The fusion of features is realized using descriptor of Information entropy and is optimized by entropy estimation. A novel Multimodal Information fusion strategy of audio emotion recognition based on kernel entropy component analysis (KECA) has been presented. The effectiveness of the proposed solution is evaluated through experimentation on two audiovisual emotion databases. Experimental results show that the proposed solution outperforms the existing methods, especially when the dimension of feature space is substantially reduced. The proposed method offers general theoretical analysis which gives us an approach to implement Information theory into multimedia research.
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Multimodal Information fusion of audiovisual emotion recognition using novel Information theoretic tools
2013Co-Authors: Zhibing Xie, Ling GuanAbstract:This paper aims at providing general theoretical analysis for the issue of Multimodal Information fusion and implementing novel Information theoretic tools in multimedia application. The most essential issues for Information fusion include feature transformation and reduction of feature dimensionality. Most previous solutions are based on the second order statistics, which is only optimal for Gaussian-like distribution, while in this paper we describe kernel entropy component analysis (KECA) which utilizes descriptor of Information entropy and achieves improved performance by entropy estimation. We present a new solution based on the integration of Information fusion theory and Information theoretic tools in this paper. The proposed method has been applied to audiovisual emotion recognition. Information fusion has been implemented for audio and video channels at feature level and decision level. Experimental results demonstrate that the proposed algorithm achieves improved performance in comparison with the existing methods, especially when the dimension of feature space is substantially reduced.
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Multimodal Information fusion of audio emotion recognition based on kernel entropy component analysis
2012Co-Authors: Zhibing Xie, Ling GuanAbstract:This paper focuses on the application of novel Information theoretic tools in the area of Information fusion. Feature transformation and fusion is critical for the performance of Information fusion, however the majority of the existing works depend on the second order statistics, which is only optimal for Gaussian-like distribution. In this paper, the integration of Information fusion techniques and kernel entropy component analysis provides a new Information theoretic tool. The fusion of features is realized using descriptor of Information entropy and optimized by entropy estimation. A novel Multimodal Information fusion strategy of audio emotion recognition based on kernel entropy component analysis (KECA) has been presented. The effectiveness of the proposed solution is evaluated though experimentation on two audiovisual emotion databases. Experimental results show that the proposed solution outperforms the existing methods, especially when the dimension of feature space is substantially reduced. The proposed method offers general theoretical analysis which gives us an approach to implement Information theory into multimedia research.
Rogers F Silva - One of the best experts on this subject based on the ideXlab platform.
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multidataset independent subspace analysis with application to Multimodal fusion
2021Co-Authors: Rogers F Silva, Sergey M Plis, Tulay Adali, Marios S Pattichis, Vince D CalhounAbstract:Unsupervised latent variable models—blind source separation (BSS) especially—enjoy a strong reputation for their interpretability. But they seldom combine the rich diversity of Information available in multiple datasets, even though multidatasets yield insightful joint solutions otherwise unavailable in isolation. We present a direct, principled approach to multidataset combination that takes advantage of multidimensional subspace structures. In turn, we extend BSS models to capture the underlying modes of shared and unique variability across and within datasets. Our approach leverages joint Information from heterogeneous datasets in a flexible and synergistic fashion. We call this method multidataset independent subspace analysis (MISA). Methodological innovations exploiting the Kotz distribution for subspace modeling, in conjunction with a novel combinatorial optimization for evasion of local minima, enable MISA to produce a robust generalization of independent component analysis (ICA), independent vector analysis (IVA), and independent subspace analysis (ISA) in a single unified model. We highlight the utility of MISA for Multimodal Information fusion, including sample-poor regimes ( $N = 600$ ) and low signal-to-noise ratio, promoting novel applications in both unimodal and Multimodal brain imaging data.
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multidataset independent subspace analysis with application to Multimodal fusion
2019Co-Authors: Rogers F Silva, Sergey M Plis, Tulay Adali, Marios S Pattichis, Vince D CalhounAbstract:In the last two decades, unsupervised latent variable models---blind source separation (BSS) especially---have enjoyed a strong reputation for the interpretable features they produce. Seldom do these models combine the rich diversity of Information available in multiple datasets. Multidatasets, on the other hand, yield joint solutions otherwise unavailable in isolation, with a potential for pivotal insights into complex systems. To take advantage of the complex multidimensional subspace structures that capture underlying modes of shared and unique variability across and within datasets, we present a direct, principled approach to multidataset combination. We design a new method called multidataset independent subspace analysis (MISA) that leverages joint Information from multiple heterogeneous datasets in a flexible and synergistic fashion. Methodological innovations exploiting the Kotz distribution for subspace modeling in conjunction with a novel combinatorial optimization for evasion of local minima enable MISA to produce a robust generalization of independent component analysis (ICA), independent vector analysis (IVA), and independent subspace analysis (ISA) in a single unified model. We highlight the utility of MISA for Multimodal Information fusion, including sample-poor regimes and low signal-to-noise ratio scenarios, promoting novel applications in both unimodal and Multimodal brain imaging data.
Mohan M Trivedi - One of the best experts on this subject based on the ideXlab platform.
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Multimodal Information fusion using the iterative decoding algorithm and its application to audio-visual speech recognition
2008Co-Authors: Shankar T Shivappa, Mohan M TrivediAbstract:The fusion of Information from heterogenous sensors is crucial to the effectiveness of a Multimodal system. Noise affect the sensors of different modalities independently. A good fusion scheme should be able to use local estimates of the reliability of each modality to weight the decisions. This paper presents an iterative decoding based Information fusion scheme motivated by the theory of turbo codes. This fusion framework is developed in the context of hidden Markov models. We present the mathematical framework of the fusion scheme. We then apply this algorithm to an audio-visual speech recognition task on the GRID audio-visual speech corpus and present the results.
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an iterative decoding algorithm for fusion of Multimodal Information
2007Co-Authors: Shankar T Shivappa, Bhaskar D Rao, Mohan M TrivediAbstract:Human activity analysis in an intelligent space is typically based on Multimodal Informational cues. Use of multiple modalities gives us a lot of advantages. But Information fusion from different sources is a problem that has to be addressed. In this paper, we propose an iterative algorithm to fuse Information from Multimodal sources. We draw inspiration from the theory of turbo codes. We draw an analogy between the redundant parity bits of the constituent codes of a turbo code and the Information from different sensors in a Multimodal system. A hidden Markov model is used to model the sequence of observations of individual modalities. The decoded state likelihoods from one modality are used as additional Information in decoding the states of the other modalities. This procedure is repeated until a certain convergence criterion is met. The resulting iterative algorithm is shown to have lower error rates than the individual models alone. The algorithm is then applied to a real-world problem of speech segmentation using audio and visual cues.
