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Using fMRI brain activation to identify cognitive states associated with perception of tools and dwellings
 Article ID e1394
, 2008
"... Previous studies have succeeded in identifying the cognitive state corresponding to the perception of a set of depicted categories, such as tools, by analyzing the accompanying pattern of brain activity, measured with fMRI. The current research focused on identifying the cognitive state associated w ..."
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Cited by 29 (6 self)
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Previous studies have succeeded in identifying the cognitive state corresponding to the perception of a set of depicted categories, such as tools, by analyzing the accompanying pattern of brain activity, measured with fMRI. The current research focused on identifying the cognitive state associated with a 4s viewing of an individual line drawing (1 of 10 familiar objects, 5 tools and 5 dwellings, such as a hammer or a castle). Here we demonstrate the ability to reliably (1) identify which of the 10 drawings a participant was viewing, based on that participant’s characteristic wholebrain neural activation patterns, excluding
Distatis: The Analysis of Multiple Distance Matrices
 Proceedings of the IEEE Computer Society: International Conference on Computer Vision and Pattern Recognition 2005
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Analyzing assessors and products in sorting tasks: DISTATIS, theory and application. Food Quality and Preference
, 2007
"... In this paper we present a new method called DISTATIS that can be applied to the analysis of sorting data. DISTATIS is a generalization of classical multidimensional scaling which allows one to analyze 3ways distance tables. When used for analyzing sorting tasks, DISTATIS takes into account individ ..."
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Cited by 10 (3 self)
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In this paper we present a new method called DISTATIS that can be applied to the analysis of sorting data. DISTATIS is a generalization of classical multidimensional scaling which allows one to analyze 3ways distance tables. When used for analyzing sorting tasks, DISTATIS takes into account individual sorting data. Specifically, when DISTATIS is used to analyze the results of an experiment in which several assessors sort a set of products, we obtain two types of maps: One for the assessors and one for the products. In these maps, the proximity between two points reflects their similarity, and therefore these maps can be read using the same rules as standard metric multidimensional scaling methods or principal component analysis. Technically, DISTATIS starts by transforming the individual sorting data into crossproduct matrices as in classical MDS and evaluating the similarity between these matrices (using Escoufier’s RV coefficient). Then it computes a compromise matrix which is the best aggregate (in the least square sense, as STATIS does) of the individual crossproduct matrices and analyzes it with PCA. The individual matrices are then projected onto the compromise space. In this paper, we present a short tutorial, and we illustrate how to use DISTATIS with a sorting task in which ten assessors evaluated eight beers. We also provide some insights into how
A simple alternative to Generalized Procrustes Analysis. Application to sensory profiling data
 Journal of Sensory Studies
, 1999
"... A statistical method for analysing sensory profiling data obtained by means of fixed vocabulary or free choice profiling is discussed. The most interesting feature of this method is that it involves only simple statistical treatment and can therefore be performed using standard software packages. Th ..."
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Cited by 8 (2 self)
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A statistical method for analysing sensory profiling data obtained by means of fixed vocabulary or free choice profiling is discussed. The most interesting feature of this method is that it involves only simple statistical treatment and can therefore be performed using standard software packages. The outcomes of this method are compared to those of Generalized Procrustes Analysis on the basis of two data sets obtained respectively by means of fixed vocabulary and free choice profiling. A significance test is also discussed in order to assess whether the overall configuration of the products is meaningful. This significance test is based upon a simulation study involving the permutation procedure. Keywords : Sensory profiling, Principal Components Analysis, Generalized Procrustes Analysis, Isotropic scaling factors, Permutation test. 3 Introduction Generalized Procrustes Analysis (GPA) was introduced and popularized by Gower (1975). It is used for the analysis of sensory profiling da...
Sparse canonical methods for biological data integration: application to a crossplatform study
 BMC Bioinformatics
"... In the context of integration for systems biology, very few sparse approaches have been proposed so far to select variables in a canonical framework. In this study we propose a canonical mode of a new sparse PLS approach to handle twoblock data sets, where the relationship between the two types of ..."
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Cited by 7 (0 self)
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In the context of integration for systems biology, very few sparse approaches have been proposed so far to select variables in a canonical framework. In this study we propose a canonical mode of a new sparse PLS approach to handle twoblock data sets, where the relationship between the two types of variables is known to be symmetric. Sparse PLS has been proposed for either a regression or a canonical mode and includes a builtin procedure to perform variable selection while integrating data. To illustrate the canonical mode approach, we analyzed the NCI60 data sets, where two different platforms (cDNA and Affymetrix chips) were used to study the transcriptome of sixty cancer cell lines. We compare the results obtained with two other sparse or related canonical approaches: CCA with Elastic Net penalization (CCAEN) and CoInertia Analysis (CIA). The latter does not include a builtin procedure for variable selection and requires a twostep analysis. We stress the lack of statistical criteria to evaluate canonical methods, which makes biological interpretation crucial to compare the different gene lists. We propose comprehensive graphical representations of both samples and variables to facilitate the biologist interpretation. We show that sPLS and CCAEN select highly relevant genes, which enable a detailed understanding of the molecular characteristics of several groups of cell lines. These two approaches were found to bring similar results, although they highlighted the same phenomenons with a different priority. On the other hand, CIA tended to select redundant information. These canonical methods seem to be efficient tools to deal with variable selection in the context of highthroughput data integration.
BioMed Central
, 2006
"... A novel approach to phylogenetic tree construction using stochastic optimization and clustering ..."
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Cited by 6 (3 self)
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A novel approach to phylogenetic tree construction using stochastic optimization and clustering
Biplots in reducedrank regression
 Biom. J
, 1994
"... SUMMARY Regression problems with a number of related response variables are typically analyzed by separate multiple regressions. This paper shows how these regressions can be visualized jointly in a biplot based on reducedrank regression. Reducedrank regression combines multiple regression and pri ..."
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Cited by 5 (0 self)
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SUMMARY Regression problems with a number of related response variables are typically analyzed by separate multiple regressions. This paper shows how these regressions can be visualized jointly in a biplot based on reducedrank regression. Reducedrank regression combines multiple regression and principal components analysis and can therefore be carried out with standard statistical packages. The proposed biplot highlights the major aspects of the regressions by displaying the leastsquares approximation of fitted values, regression coefficients and associated tratio's. The utility and interpretation of the reducedrank regression biplot is demonstrated with an example using public health data that were previously analyzed by separate multiple regressions.
Criteria for evaluating dimensionreducing components for multivariate data
 The American Statistician
, 2004
"... Gasser for many fruitful discussions, the referees for their constructive criticism, and the editor and an associate editor for suggestions to improve the presentation. Principal components are the benchmark for linear dimension reduction, but they are not always easy to interpret. For this reason, ..."
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Cited by 3 (1 self)
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Gasser for many fruitful discussions, the referees for their constructive criticism, and the editor and an associate editor for suggestions to improve the presentation. Principal components are the benchmark for linear dimension reduction, but they are not always easy to interpret. For this reason, some alternatives have been proposed in recent years. These methods produce components that, unlike principal components, are correlated and/or have nonorthogonal loadings. We show in this article that the criteria commonly used to evaluate principal components are not adequate for evaluating such components, and propose two new criteria that are more suitable for this purpose.
Some measures of agreement between close partitions
 Student
, 2004
"... Abstract. In order to measure the similarity between two partitions coming from the same data set, we study extensions of the RVcoefficient, the kappa coefficient proposed by Cohen (in case of partitions with same number of classes), and the D2 coefficient proposed by Popping. We find that the RV c ..."
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Cited by 1 (0 self)
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Abstract. In order to measure the similarity between two partitions coming from the same data set, we study extensions of the RVcoefficient, the kappa coefficient proposed by Cohen (in case of partitions with same number of classes), and the D2 coefficient proposed by Popping. We find that the RV coefficient is identical to the Janson and Vegelius index. We compare the result coming from kappa’s coefficient to the ordination given by correspondence analysis. We study the empirical distribution of these indices under the hypotheses of a common partition. For this purpose, we use data coming from a latent profile model to formulate the null hypothesis. Keywords. RV, Janson and Vegelius index, Cohen’s Kappa, Popping ’ D2, Correspondence analysis, latent class