298 research outputs found
Matrix and Stimulus Sample Sizes in the Weighted MDS Model: Empirical Metric Recovery Functions
The only guidelines for sample size that exist in the multidimensional scaling (MDS) literature are a set of heuristic "rules-of-thumb" that have failed to live up to Young's (1970) goal of finding func tional relationships between sample size and metric recovery. This paper develops answers to two im portant sample-size questions in nonmetric weight ed MDS settings, both of which are extensions of work reported in MacCallum and Cornelius (1977): (1) are the sample size requirements for number of stimuli and number of matrices compensatory? and (2) what type of functional relationships exist between the number of matrices and metric recov ery ? The graphs developed to answer the second question illustrate how such functional relation ships can be defined empirically in a wide range of MDS and other complicated nonlinear models.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline
Non-Gaussian CMBR angular power spectra
In this paper we show how the prediction of CMBR angular power spectra
in non-Gaussian theories is affected by a cosmic covariance problem, that is
correlations impart features on any observed spectrum
which are absent from the average spectrum. Therefore the average
spectrum is rendered a bad observational prediction, and two new prediction
strategies, better adjusted to these theories, are proposed. In one we search
for hidden random indices conditional to which the theory is released from the
correlations. Contact with experiment can then be made in the form of the
conditional power spectra plus the random index distribution. In another
approach we apply to the problem a principal component analysis. We discuss the
effect of correlations on the predictivity of non-Gaussian theories. We finish
by showing how correlations may be crucial in delineating the borderline
between predictions made by non-Gaussian and Gaussian theories. In fact, in
some particular theories, correlations may act as powerful non-Gaussianity
indicators
Computationally Efficient Implementation of Convolution-based Locally Adaptive Binarization Techniques
One of the most important steps of document image processing is binarization.
The computational requirements of locally adaptive binarization techniques make
them unsuitable for devices with limited computing facilities. In this paper,
we have presented a computationally efficient implementation of convolution
based locally adaptive binarization techniques keeping the performance
comparable to the original implementation. The computational complexity has
been reduced from O(W2N2) to O(WN2) where WxW is the window size and NxN is the
image size. Experiments over benchmark datasets show that the computation time
has been reduced by 5 to 15 times depending on the window size while memory
consumption remains the same with respect to the state-of-the-art algorithmic
implementation
Multi-score Learning for Affect Recognition: the Case of Body Postures
An important challenge in building automatic affective state
recognition systems is establishing the ground truth. When the groundtruth
is not available, observers are often used to label training and testing
sets. Unfortunately, inter-rater reliability between observers tends to
vary from fair to moderate when dealing with naturalistic expressions.
Nevertheless, the most common approach used is to label each expression
with the most frequent label assigned by the observers to that expression.
In this paper, we propose a general pattern recognition framework
that takes into account the variability between observers for automatic
affect recognition. This leads to what we term a multi-score learning
problem in which a single expression is associated with multiple values
representing the scores of each available emotion label. We also propose
several performance measurements and pattern recognition methods for
this framework, and report the experimental results obtained when testing
and comparing these methods on two affective posture datasets
Adaptive Sampling for Nonlinear Dimensionality Reduction Based on Manifold Learning
We make use of the non-intrusive dimensionality reduction method Isomap in order to emulate nonlinear parametric flow problems that are governed by the Reynolds-averaged Navier-Stokes equations. Isomap is a manifold learning approach that provides a low-dimensional embedding space that is approximately isometric to the manifold that is assumed to be formed by the high-fidelity Navier-Stokes flow solutions under smooth variations of the inflow conditions. The focus of the work at hand is the adaptive construction and refinement of the Isomap emulator: We exploit the non-Euclidean Isomap metric to detect and fill up gaps in the sampling in the embedding space. The performance of the proposed manifold filling method will be illustrated by numerical experiments, where we consider nonlinear parameter-dependent steady-state Navier-Stokes flows in the transonic regime
Seriation and Multidimensional Scaling: A Data Analysis Approach to Scaling Asymmetric Proximity Matrices
A number of model-based scaling methods have been developed that apply to asymmetric proximity matrices. A flexible data analysis approach is pro posed that combines two psychometric procedures— seriation and multidimensional scaling (MDS). The method uses seriation to define an empirical order ing of the stimuli, and then uses MDS to scale the two separate triangles of the proximity matrix defined by this ordering. The MDS solution con tains directed distances, which define an "extra" dimension that would not otherwise be portrayed, because the dimension comes from relations between the two triangles rather than within triangles. The method is particularly appropriate for the analysis of proximities containing temporal information. A major difficulty is the computa tional intensity of existing seriation algorithms, which is handled by defining a nonmetric seriation algorithm that requires only one complete itera tion. The procedure is illustrated using a matrix of co-citations between recent presidents of the Psychometric Society.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline
Multivariate Small Area Estimation of Multidimensional Latent Economic Well-being Indicators
© 2019 The Authors. International Statistical Review © 2019 International Statistical Institute Factor analysis models are used in data dimensionality reduction problems where the variability among observed variables can be described through a smaller number of unobserved latent variables. This approach is often used to estimate the multidimensionality of well-being. We employ factor analysis models and use multivariate empirical best linear unbiased predictor (EBLUP) under a unit-level small area estimation approach to predict a vector of means of factor scores representing well-being for small areas. We compare this approach with the standard approach whereby we use small area estimation (univariate and multivariate) to estimate a dashboard of EBLUPs of the means of the original variables and then averaged. Our simulation study shows that the use of factor scores provides estimates with lower variability than weighted and simple averages of standardised multivariate EBLUPs and univariate EBLUPs. Moreover, we find that when the correlation in the observed data is taken into account before small area estimates are computed, multivariate modelling does not provide large improvements in the precision of the estimates over the univariate modelling. We close with an application using the European Union Statistics on Income and Living Conditions data
Sufficient Covariate, Propensity Variable and Doubly Robust Estimation
Statistical causal inference from observational studies often requires
adjustment for a possibly multi-dimensional variable, where dimension reduction
is crucial. The propensity score, first introduced by Rosenbaum and Rubin, is a
popular approach to such reduction. We address causal inference within Dawid's
decision-theoretic framework, where it is essential to pay attention to
sufficient covariates and their properties. We examine the role of a propensity
variable in a normal linear model. We investigate both population-based and
sample-based linear regressions, with adjustments for a multivariate covariate
and for a propensity variable. In addition, we study the augmented inverse
probability weighted estimator, involving a combination of a response model and
a propensity model. In a linear regression with homoscedasticity, a propensity
variable is proved to provide the same estimated causal effect as multivariate
adjustment. An estimated propensity variable may, but need not, yield better
precision than the true propensity variable. The augmented inverse probability
weighted estimator is doubly robust and can improve precision if the propensity
model is correctly specified
Exploratory fMRI analysis without spatial normalization
Author Manuscript received 2010 March 11. 21st International Conference, IPMI 2009, Williamsburg, VA, USA, July 5-10, 2009. ProceedingsWe present an exploratory method for simultaneous parcellation of multisubject fMRI data into functionally coherent areas. The method is based on a solely functional representation of the fMRI data and a hierarchical probabilistic model that accounts for both inter-subject and intra-subject forms of variability in fMRI response. We employ a Variational Bayes approximation to fit the model to the data. The resulting algorithm finds a functional parcellation of the individual brains along with a set of population-level clusters, establishing correspondence between these two levels. The model eliminates the need for spatial normalization while still enabling us to fuse data from several subjects. We demonstrate the application of our method on a visual fMRI study.McGovern Institute for Brain Research at MIT. Neurotechnology ProgramNational Science Foundation (U.S.) (CAREER Grant 0642971)National Institutes of Health (U.S.) (NIBIB NAMIC U54-EB005149)National Institutes of Health (U.S.) (NCRR NAC P41-RR13218
Statistical Tests of Group Differences in ALSCAL-Derived Subject Weights: Some Monte Carlo Results
Several techniques to test for group differences in weighted multidimensional scaling (MDS) subject weights have recently been proposed. The present study presents monte carlo results to evaluate the op erating characteristics of two of these with ALSCAL- derived subject weights. The first uses the analysis of angular variation (ANAVA) on raw subject weights. The second applies the analysis of variance (ANOVA) to the flattened subject weights generated by ALSCAL. The ANOVA on flattened weights was less affected by the presence of error and by distortions caused by ALSCAL'S normalization routine than was the ANAVA.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline
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