The Experts below are selected from a list of 282 Experts worldwide ranked by ideXlab platform

D. Marco - One of the best experts on this subject based on the ideXlab platform.

  • Low-Resolution Scalar Quantization for Gaussian Sources and Absolute Error
    IEEE Transactions on Information Theory, 2007
    Co-Authors: D. Marco
    Abstract:

    This correspondence considers low-resolution scalar quantization for a memoryless Gaussian source with respect to Absolute Error distortion. It shows that slope of the operational rate-distortion function of scalar quantization is infinite at the point Dmax where the rate becomes zero. Thus, unlike the situation for squared Error distortion, or for Laplacian and exponential sources with squared or Absolute Error distortion, for a Gaussian source and Absolute Error, scalar quantization at low rates is far from the Shannon rate-distortion function, i.e., far from the performance of the best lossy coding technique

  • ISIT - Low Rate Scalar Quantization for Gaussian Sources and Absolute Error
    2006 IEEE International Symposium on Information Theory, 2006
    Co-Authors: D. Marco
    Abstract:

    This paper considers low resolution scalar quantization for a memoryless Gaussian source with respect to Absolute Error distortion. It shows that slope of the the operational rate-distortion function of scalar quantization is infinite at the point Dmax where the rate becomes zero. Thus, unlike the situation for squared Error distortion, or for Laplacian and exponential sources with squared or Absolute Error distortion, for a Gaussian source and Absolute Error, scalar quantization at low rates is far from the Shannon rate-distortion function, i.e. far from the performance of the best lossy coding technique.

J. Kozowski - One of the best experts on this subject based on the ideXlab platform.

  • Instrumental-variable least-Absolute-Error algorithms for parameter estimation of continuous-time systems
    1999 European Control Conference (ECC), 1999
    Co-Authors: Zdzisław Kowalczuk, J. Kozowski
    Abstract:

    The continuous-time (c-t) approach [good, saga, unbe1-2, youn] to identification of linear continuous-time models in the sense of least-Absolute Error (LA) and instrumental variable least-Absolute Error (IvLA) is under consideration. Estimates of the system parameters are obtained based on ‘finite-horizon’ formulation of the regression vector, which contains consecutive multiple ‘integrals’ of the input and output signals. The derived continuous-time algorithms and their continuous-time regression vectors are discretised and exercised numerically.

  • ECC - Instrumental-variable least-Absolute-Error algorithms for parameter estimation of continuous-time systems
    1999 European Control Conference (ECC), 1999
    Co-Authors: Zdzisław Kowalczuk, J. Kozowski
    Abstract:

    The continuous-time (c-t) approach [good, saga, unbe1-2, youn] to identification of linear continuous-time models in the sense of least-Absolute Error (LA) and instrumental variable least-Absolute Error (IvLA) is under consideration. Estimates of the system parameters are obtained based on ‘finite-horizon’ formulation of the regression vector, which contains consecutive multiple ‘integrals’ of the input and output signals. The derived continuous-time algorithms and their continuous-time regression vectors are discretised and exercised numerically.

Y Neuvo - One of the best experts on this subject based on the ideXlab platform.

  • Optimal parallel stack filtering under the mean Absolute Error criterion
    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 1994
    Co-Authors: Bing Zeng, Y Neuvo
    Abstract:

    The authors extend the configuration of stack filtering to develop a new class of stack-type filters called parallel stack filters (PSFs). As a basis for the parallel stack filtering, the block threshold decomposition (BTD) is introduced, and its properties are investigated. The design of optimal PSHs under the mean Absolute Error (MAE) criterion is shown to be similar to the minimum MAE stack filtering theory. The only difference is that one needs now to design more than one stack filter that together construct an optimal PSF. As a result, while reviewing briefly the optimal stack filtering theory, they will put more efforts to demonstrate, via several examples, the improvement by switching from stack filtering to parallel stack filtering for the task of image noise removal. >

  • Adaptive generalized stack filtering under the mean-Absolute-Error criterion
    Nonlinear Image Processing III, 1992
    Co-Authors: L Yin, Jaakko Astola, Y Neuvo
    Abstract:

    A new adaptive algorithm is developed in this paper for determining optimal generalized stack (GS) filters under the mean Absolute Error criterion. This algorithm, based on the neural network representation of Boolean functions, is much more efficient than the traditional truth table based algorithms. This is because: (1) the number of variables to represent a GS filter is considerably reduced when a set of neurons is used to represent a GS filter, where the number of the variables is proportional to the filter window width, and (2) the procedure of enforcing the stacking constraints of GS filters is greatly simplified since a sufficient condition is derived under which the neurons satisfy the stacking property. Experimental results from image restoration are provided to demonstrate the performance of the new adaptive GS filters.

  • optimal weighted order statistic filters under the mean Absolute Error criterion
    International Conference on Acoustics Speech and Signal Processing, 1991
    Co-Authors: L Yin, Jaakko Astola, Y Neuvo
    Abstract:

    Based on the relationship between weighted order statistic (WOS) filters and threshold logic, an algorithm is developed for determining optimal WOS filters under the mean Absolute Error (MAE) criterion. This algorithm requires much less computation than the adaptive stack filtering algorithm. In addition, experimental results in image restoration demonstrate that the WOS filters obtained by the proposed algorithm can even give better results than the adaptive stack filters. >

  • ICASSP - Optimal weighted order statistic filters under the mean Absolute Error criterion
    [Proceedings] ICASSP 91: 1991 International Conference on Acoustics Speech and Signal Processing, 1991
    Co-Authors: L Yin, Jaakko Astola, Y Neuvo
    Abstract:

    Based on the relationship between weighted order statistic (WOS) filters and threshold logic, an algorithm is developed for determining optimal WOS filters under the mean Absolute Error (MAE) criterion. This algorithm requires much less computation than the adaptive stack filtering algorithm. In addition, experimental results in image restoration demonstrate that the WOS filters obtained by the proposed algorithm can even give better results than the adaptive stack filters. >

Hiroshi Yasukawa - One of the best experts on this subject based on the ideXlab platform.

  • eog related noise rejection in eeg signal with eye movement task by tensor product expansion with Absolute Error
    European Signal Processing Conference, 2010
    Co-Authors: Akitoshi Itai, Arao Funase, Andrzej Cichocki, Hiroshi Yasukawa
    Abstract:

    Eye movement origin electrooculogram (EOG) artifacts yield significant problems for the saccade-related electroencephalogram (EEG) and its analysis. The denoising of EOG artifacts is important task to analyze the relationship between a saccade and a brain function. In this paper, a tensor product expansion with Absolute Error (TPE-AE) is applied to reduce the EOG artifacts from the EEG signal. The TPE-AE has some difficulty to separate the EOG from the saccade-related EEG data due to a background noise from a spontaneous EEG activity. We show that the TPE-AE, which calculates two outer products, is useful to reduce EOG components related to the eye movement.

  • global noise estimation based on tensor product expansion with Absolute Error
    IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences, 2007
    Co-Authors: Akitoshi Itai, Hiroshi Yasukawa, Ichi Takumi, Masayasu Hata
    Abstract:

    This paper proposes a novel signal estimation method that uses a tensor product expansion. When a bivariable function, which is expressed by two-dimensional matrix, is subjected to conventional tensor product expansion, two single variable functions are calculated by minimizing the mean square Error between the input vector and its outer product. A tensor product expansion is useful for feature extraction and signal compression, however, it is difficult to separate global noise from other signals. This paper shows that global noise, which is observed in almost all input signals, can be estimated by using a tensor product expansion where Absolute Error is used as the Error function.

Tianfeng Chai - One of the best experts on this subject based on the ideXlab platform.

  • root mean square Error rmse or mean Absolute Error mae arguments against avoiding rmse in the literature
    Geoscientific Model Development, 2014
    Co-Authors: Tianfeng Chai, Roland R. Draxler
    Abstract:

    Abstract. Both the root mean square Error (RMSE) and the mean Absolute Error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average Error, and thus the MAE would be a better metric for that purpose. While some concerns over using RMSE raised by Willmott and Matsuura (2005) and Willmott et al. (2009) are valid, the proposed avoidance of RMSE in favor of MAE is not the solution. Citing the aforementioned papers, many researchers chose MAE over RMSE to present their model evaluation statistics when presenting or adding the RMSE measures could be more beneficial. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the Error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric, whereas Willmott et al. (2009) indicated that the sums-of-squares-based statistics do not satisfy this rule. In the end, we discussed some circumstances where using the RMSE will be more beneficial. However, we do not contend that the RMSE is superior over the MAE. Instead, a combination of metrics, including but certainly not limited to RMSEs and MAEs, are often required to assess model performance.

  • Root mean square Error (RMSE) or mean Absolute Error (MAE)
    2014
    Co-Authors: Tianfeng Chai, Roland R. Draxler
    Abstract:

    Abstract. Both the root mean square Error (RMSE) and the mean Absolute Error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average Error and thus the MAE would be a better metric for that purpose. Their paper has been widely cited and may have influenced many researchers in choosing MAE when presenting their model evaluation statistics. However, we contend that the proposed avoidance of RMSE and the use of MAE is not the solution to the problem. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the Error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric.