The Experts below are selected from a list of 201468 Experts worldwide ranked by ideXlab platform

S. Puthusserypady - One of the best experts on this subject based on the ideXlab platform.

  • A sparse Bayesian method for determination of flexible Design Matrix for fMRI data analysis
    IEEE Transactions on Circuits and Systems I: Regular Papers, 2005
    Co-Authors: S. Puthusserypady
    Abstract:

    The construction of a Design Matrix is critical to the accurate detection of activation regions of the brain in functional magnetic resonance imaging (fMRI). The Design Matrix should be flexible to capture the unknown slowly varying drifts as well as robust enough to avoid overfitting. In this paper, a sparse Bayesian learning method is proposed to determine a suitable Design Matrix for fMRI data analysis. Based on a generalized linear model, this learning method lets the data itself determine the form of the regressors in the Design Matrix. It automatically finds those regressors that are relevant to the generation of the fMRI data and discards the others that are irrelevant. The proposed approach integrates the advantages of currently employed methods of fMRI data analysis (the model-driven and the data-driven methods). Results from the simulation studies clearly reveal the superiority of the proposed scheme to the conventional t-test method of fMRI data analysis.

Sekhar Tatikonda - One of the best experts on this subject based on the ideXlab platform.

  • Scatter Matrix Concordance: A Diagnostic for Regressions on Subsets of Data
    arXiv: Machine Learning, 2015
    Co-Authors: Michael Kane, Bryan W. Lewis, Sekhar Tatikonda, Simon Urbanek
    Abstract:

    Linear regression models depend directly on the Design Matrix and its properties. Techniques that efficiently estimate model coefficients by partitioning rows of the Design Matrix are increasingly popular for large-scale problems because they fit well with modern parallel computing architectures. We propose a simple measure of {\em concordance} between a Design Matrix and a subset of its rows that estimates how well a subset captures the variance-covariance structure of a larger data set. We illustrate the use of this measure in a heuristic method for selecting row partition sizes that balance statistical and computational efficiency goals in real-world problems.

  • lossy compression via sparse linear regression computationally efficient encoding and decoding
    International Symposium on Information Theory, 2013
    Co-Authors: Ramji Venkataramanan, Tuhin Sarkar, Sekhar Tatikonda
    Abstract:

    We propose computationally efficient encoders and decoders for lossy compression using a Sparse Regression Code. Codewords are structured linear combinations of columns of a Design Matrix. The proposed encoding algorithm sequentially chooses columns of the Design Matrix to successively approximate the source sequence. It is shown to achieve the optimal distortion-rate function for i.i.d Gaussian sources with squared-error distortion. For a given rate, the parameters of the Design Matrix can be varied to trade off distortion performance with encoding complexity. An example of such a trade-off is: computational resource (space or time) per source sample of O((n/ log n)2) and probability of excess distortion decaying exponentially in n/ log n, where n is the block length. The Sparse Regression Code is robust in the following sense: for any ergodic source, the proposed encoder achieves the optimal distortion-rate function of an i.i.d Gaussian source with the same variance. Simulations show that the encoder has very good empirical performance, especially at low and moderate rates.

Sílvio Rogério Correia Freitas - One of the best experts on this subject based on the ideXlab platform.

  • Influence of errors in coordinate transformation due to uncertainties of the Design Matrix
    Applied Geomatics, 2013
    Co-Authors: Henry Montecino Castro, Sebastian Santibanez, Sílvio Rogério Correia Freitas
    Abstract:

    Coordinate transformation between reference systems is a habitual task in geodetic sciences. Several transformation models and adjustment of observations algorithms have been explored. The most popular approach uses the well-known Ordinary Least Squares method (OLS) which seeks to estimate unknown parameters in a linear regression model by minimizing the sum of squared residuals while assuming that the observations are subject to normally distributed random noise. Although this approach has been widely used on many applications, its assumption regarding the distribution of errors is unrealistic. An alternative model created to deal with non normal–random distributions of errors is the Total Least Square model (TLS). Although several approaches have been suggested for representing the errors in the Design Matrix, most of them fail in including errors with different stochastic characteristics truthfully. This paper presents an assessment of the quality of a Helmert transformation of 2D coordinates when different precisions in both the observation vector and the Design Matrix exist. Two algorithms were analyzed: the OLS, and the Improved Weighted Total Least Squares (IWTLS). The transformation parameters obtained through IWTLS and OLS show significant differences among them when the variances in the coordinate systems are different. When assessing the transformation of coordinates using control points, it becomes clear that both algorithms perform the same in terms of the quality of the transformation. Furthermore, it is shown that an analysis based on the residuals obtained in the least squares adjustment is not a reliable tool to assess the overall quality of the transformation.

Tamio Arai - One of the best experts on this subject based on the ideXlab platform.

  • Architecture Design and Assessment with Design Matrix
    2011 IEEE World Congress on Services, 2011
    Co-Authors: Shigeru Hosono, Koji Kimita, Fumiya Akasaka, Tatsunori Hara, Yoshiki Shimomura, Min Min, Tamio Arai
    Abstract:

    Practices of developing applications based on SOA principles need to be renovated to meet new requirements for large-scale data processing and to adapt advancements in IT infrastructures in cloud computing. Resource planners will have a significant role as they bridge the development and operation phases in application development. To accomplish these tasks, this paper presents architecture Design methods, which reinforce Designing and assessing architecture with an applied Design Structure Matrix (DSM). This approach can embrace development of both business logics of applications and IT resources to run them in the cloud and the methods are established through consulting practices on a datacenter.

  • SERVICES - Architecture Design and Assessment with Design Matrix
    2011 IEEE World Congress on Services, 2011
    Co-Authors: Shigeru Hosono, Koji Kimita, Fumiya Akasaka, Tatsunori Hara, Yoshiki Shimomura, Min Min, Tamio Arai
    Abstract:

    Practices of developing applications based on SOA principles need to be renovated to meet new requirements for large-scale data processing and to adapt advancements in IT infrastructures in cloud computing. Resource planners will have a significant role as they bridge the development and operation phases in application development. To accomplish these tasks, this paper presents architecture Design methods, which reinforce Designing and assessing architecture with an applied Design Structure Matrix (DSM). This approach can embrace development of both business logics of applications and IT resources to run them in the cloud and the methods are established through consulting practices on a datacenter.

Xing Fang - One of the best experts on this subject based on the ideXlab platform.

  • robust total least squares with reweighting iteration for three dimensional similarity transformation
    Survey Review, 2014
    Co-Authors: B F Li, Xing Fang
    Abstract:

    AbstractTo resist the influence of gross errors in observations on the adjusted parameters, the robust Least Squares (LS) adjustment has been extensively studied and successfully applied in the real applications. However, in the LS adjustment, the Design Matrix is treated as non-random even if its elements come from the real observations that are in general inevitably error-contaminated. Such assumption will lead to the incorrect solution if the gross error exists in the observations of Design Matrix. In this paper, we study the robust Total Least Squares (TLS) adjustment, where observation errors in Design Matrix are taken into account. The reweighting iteration robust scheme is applied to detect and identify the blundered observation equations as well as reweight them, obtaining the reliable TLS solution. The example of three-dimensional similarity coordinate transformation is carried out to demonstrate the performance of the presented robust TLS. The result shows that the robust TLS can indeed resist t...