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Gerald Friedland - One of the best experts on this subject based on the ideXlab platform.

  • An adaptive Initialization Method for speaker Diarization based on prosodic features
    2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010
    Co-Authors: David Imseng, Gerald Friedland
    Abstract:

    The following article presents a novel, adaptive Initialization scheme that can be applied to most state-of-the-art Speaker Diarization algorithms, i.e. algorithms that use agglomerative hierarchical clustering with Bayesian Information Criterion (BIC) and Gaussian Mixture Models (GMMs) of frame-based cepstral features (MFCCs). The Initialization Method is a combination of the recently proposed “adaptive seconds per Gaussian” (ASPG) Method and a new pre-clustering and number of initial clusters estimation Method based on prosodic features. The presented Initialization Method has two important advantages. First, the Method requires no manual tuning and is robust against file length and speaker count variations. Second, the Method outperforms our previously used Initialization Methods on all benchmark files that were presented in the 2006, 2007, and 2009 NIST Rich Transcription (RT) evaluations and results in a Diarization Error Rate (DER) improvement of up to 67% (relative).

  • ICASSP - An adaptive Initialization Method for speaker Diarization based on prosodic features
    2010 IEEE International Conference on Acoustics Speech and Signal Processing, 2010
    Co-Authors: David Imseng, Gerald Friedland
    Abstract:

    The following article presents a novel, adaptive Initialization scheme that can be applied to most state-of-the-art Speaker Diarization algorithms, i.e. algorithms that use agglomerative hierarchical clustering with Bayesian Information Criterion (BIC) and Gaussian Mixture Models (GMMs) of frame-based cepstral features (MFCCs). The Initialization Method is a combination of the recently proposed “adaptive seconds per Gaussian” (ASPG) Method and a new pre-clustering and number of initial clusters estimation Method based on prosodic features. The presented Initialization Method has two important advantages. First, the Method requires no manual tuning and is robust against file length and speaker count variations. Second, the Method outperforms our previously used Initialization Methods on all benchmark files that were presented in the 2006, 2007, and 2009 NIST Rich Transcription (RT) evaluations and results in a Diarization Error Rate (DER) improvement of up to 67% (relative).

N. Sauwen - One of the best experts on this subject based on the ideXlab platform.

  • the successive projection algorithm as an Initialization Method for brain tumor segmentation using non negative matrix factorization
    PLOS ONE, 2017
    Co-Authors: N. Sauwen, M. Acou, H.n. Bharath, Eric Achten, Jelle Veraart, Uwe Himmelreich, Frederik Maes, Diana Sima, Sabine Van Huffel
    Abstract:

    Non-negative matrix factorization (NMF) has become a widely used tool for additive parts-based analysis in a wide range of applications. As NMF is a non-convex problem, the quality of the solution will depend on the Initialization of the factor matrices. In this study, the successive projection algorithm (SPA) is proposed as an Initialization Method for NMF. SPA builds on convex geometry and allocates endmembers based on successive orthogonal subspace projections of the input data. SPA is a fast and reproducible Method, and it aligns well with the assumptions made in near-separable NMF analyses. SPA was applied to multi-parametric magnetic resonance imaging (MRI) datasets for brain tumor segmentation using different NMF algorithms. Comparison with common Initialization Methods shows that SPA achieves similar segmentation quality and it is competitive in terms of convergence rate. Whereas SPA was previously applied as a direct endmember extraction tool, we have shown improved segmentation results when using SPA as an Initialization Method, as it allows further enhancement of the sources during the NMF iterative procedure.

  • The successive projection algorithm as an Initialization Method for brain tumor segmentation using non-negative matrix factorization
    PLoS ONE, 2017
    Co-Authors: N. Sauwen, M. Acou, H.n. Bharath, Diana M. Sima, Eric Achten, Jelle Veraart, Uwe Himmelreich, Frederik Maes, Sabine Van Huffel
    Abstract:

    © 2017 Sauwen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Non-negative matrix factorization (NMF) has become a widely used tool for additive parts-based analysis in a wide range of applications. As NMF is a non-convex problem, the quality of the solution will depend on the Initialization of the factor matrices. In this study, the successive projection algorithm (SPA) is proposed as an Initialization Method for NMF. SPA builds on convex geometry and allocates endmembers based on successive orthogonal subspace projections of the input data. SPA is a fast and reproducible Method, and it aligns well with the assumptions made in near-separable NMF analyses. SPA was applied to multi-parametric magnetic resonance imaging (MRI) datasets for brain tumor segmentation using different NMF algorithms. Comparison with common Initialization Methods shows that SPA achieves similar segmentation quality and it is competitive in terms of convergence rate. Whereas SPA was previously applied as a direct endmember extraction tool, we have shown improved segmentation results when using SPA as an Initialization Method, as it allows further enhancement of the sources during the NMF iterative procedure.

Sabine Van Huffel - One of the best experts on this subject based on the ideXlab platform.

  • The successive projection algorithm as an Initialization Method for brain tumor segmentation using non-negative matrix factorization
    PLoS ONE, 2017
    Co-Authors: N. Sauwen, M. Acou, H.n. Bharath, Diana M. Sima, Eric Achten, Jelle Veraart, Uwe Himmelreich, Frederik Maes, Sabine Van Huffel
    Abstract:

    © 2017 Sauwen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Non-negative matrix factorization (NMF) has become a widely used tool for additive parts-based analysis in a wide range of applications. As NMF is a non-convex problem, the quality of the solution will depend on the Initialization of the factor matrices. In this study, the successive projection algorithm (SPA) is proposed as an Initialization Method for NMF. SPA builds on convex geometry and allocates endmembers based on successive orthogonal subspace projections of the input data. SPA is a fast and reproducible Method, and it aligns well with the assumptions made in near-separable NMF analyses. SPA was applied to multi-parametric magnetic resonance imaging (MRI) datasets for brain tumor segmentation using different NMF algorithms. Comparison with common Initialization Methods shows that SPA achieves similar segmentation quality and it is competitive in terms of convergence rate. Whereas SPA was previously applied as a direct endmember extraction tool, we have shown improved segmentation results when using SPA as an Initialization Method, as it allows further enhancement of the sources during the NMF iterative procedure.

Sabine Van Huffel - One of the best experts on this subject based on the ideXlab platform.

  • the successive projection algorithm as an Initialization Method for brain tumor segmentation using non negative matrix factorization
    PLOS ONE, 2017
    Co-Authors: N. Sauwen, M. Acou, H.n. Bharath, Eric Achten, Jelle Veraart, Uwe Himmelreich, Frederik Maes, Diana Sima, Sabine Van Huffel
    Abstract:

    Non-negative matrix factorization (NMF) has become a widely used tool for additive parts-based analysis in a wide range of applications. As NMF is a non-convex problem, the quality of the solution will depend on the Initialization of the factor matrices. In this study, the successive projection algorithm (SPA) is proposed as an Initialization Method for NMF. SPA builds on convex geometry and allocates endmembers based on successive orthogonal subspace projections of the input data. SPA is a fast and reproducible Method, and it aligns well with the assumptions made in near-separable NMF analyses. SPA was applied to multi-parametric magnetic resonance imaging (MRI) datasets for brain tumor segmentation using different NMF algorithms. Comparison with common Initialization Methods shows that SPA achieves similar segmentation quality and it is competitive in terms of convergence rate. Whereas SPA was previously applied as a direct endmember extraction tool, we have shown improved segmentation results when using SPA as an Initialization Method, as it allows further enhancement of the sources during the NMF iterative procedure.

Frederik Maes - One of the best experts on this subject based on the ideXlab platform.

  • the successive projection algorithm as an Initialization Method for brain tumor segmentation using non negative matrix factorization
    PLOS ONE, 2017
    Co-Authors: N. Sauwen, M. Acou, H.n. Bharath, Eric Achten, Jelle Veraart, Uwe Himmelreich, Frederik Maes, Diana Sima, Sabine Van Huffel
    Abstract:

    Non-negative matrix factorization (NMF) has become a widely used tool for additive parts-based analysis in a wide range of applications. As NMF is a non-convex problem, the quality of the solution will depend on the Initialization of the factor matrices. In this study, the successive projection algorithm (SPA) is proposed as an Initialization Method for NMF. SPA builds on convex geometry and allocates endmembers based on successive orthogonal subspace projections of the input data. SPA is a fast and reproducible Method, and it aligns well with the assumptions made in near-separable NMF analyses. SPA was applied to multi-parametric magnetic resonance imaging (MRI) datasets for brain tumor segmentation using different NMF algorithms. Comparison with common Initialization Methods shows that SPA achieves similar segmentation quality and it is competitive in terms of convergence rate. Whereas SPA was previously applied as a direct endmember extraction tool, we have shown improved segmentation results when using SPA as an Initialization Method, as it allows further enhancement of the sources during the NMF iterative procedure.

  • The successive projection algorithm as an Initialization Method for brain tumor segmentation using non-negative matrix factorization
    PLoS ONE, 2017
    Co-Authors: N. Sauwen, M. Acou, H.n. Bharath, Diana M. Sima, Eric Achten, Jelle Veraart, Uwe Himmelreich, Frederik Maes, Sabine Van Huffel
    Abstract:

    © 2017 Sauwen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Non-negative matrix factorization (NMF) has become a widely used tool for additive parts-based analysis in a wide range of applications. As NMF is a non-convex problem, the quality of the solution will depend on the Initialization of the factor matrices. In this study, the successive projection algorithm (SPA) is proposed as an Initialization Method for NMF. SPA builds on convex geometry and allocates endmembers based on successive orthogonal subspace projections of the input data. SPA is a fast and reproducible Method, and it aligns well with the assumptions made in near-separable NMF analyses. SPA was applied to multi-parametric magnetic resonance imaging (MRI) datasets for brain tumor segmentation using different NMF algorithms. Comparison with common Initialization Methods shows that SPA achieves similar segmentation quality and it is competitive in terms of convergence rate. Whereas SPA was previously applied as a direct endmember extraction tool, we have shown improved segmentation results when using SPA as an Initialization Method, as it allows further enhancement of the sources during the NMF iterative procedure.