Multivariate Mapping Research Articles

Optimizing Multivariate Performance Measures from Multi-View Data

To date, many machine learning applications have multiple views of features, and different applications require specific multivariate performance measures, such as the F-score for retrieval. However, existing multivariate performance measure optimization methods are limited to single-view data, while traditional multi-view learning methods cannot optimize multivariate performance measures directly. To fill this gap, in this paper, we propose the problem of optimizing multivariate performance measures from multi-view data, and an effective method to solve it. We propose to learn linear discriminant functions for different views, and combine them to construct an overall multivariate mapping function for multi-view data. To learn the parameters of the linear discriminant functions of different views to optimize a given multivariate performance measure, we formulate an optimization problem. In this problem, we propose to minimize the complexity of the linear discriminant function of each view, promote the consistency of the responses of different views over the same data points, and minimize the upper boundary of the corresponding loss of a given multivariate performance measure. To optimize this problem, we develop an iterative cutting-plane algorithm. Experiments on four benchmark data sets show that it not only outperforms traditional single-view based multivariate performance optimization methods, but also achieves better results than ordinary multi-view learning methods.

Near-infrared chemical imaging (NIR-CI) on roll compacted ribbons and tablets – multivariate mapping of physical and chemical properties

Near-infrared chemical imaging (NIR-CI) is an attractive technique within the pharmaceutical industry, where tools are continuously in demand to assess the quality of the intermediate and final products. The present paper demonstrates how NIR-CI in combination with multivariate methods was utilized to spatially map physical properties and content of roll compacted ribbons and tablets. Additionally, extracted textural parameters from tablet images were correlated to the design parameters of the roll compaction process as well as to the physical properties of the granules. The results established the use of NIR-CI as a complementary nondestructive tool to determine the ribbon density and map the density distribution across the width and along the length of the ribbons. For the tablets, the compaction pressure developed during compression increased with the lateral distance from the center. Therefore, NIR-CI can be an effective tool to provide information about the spatial distribution of the compaction pressures on the surface of the tablet. Moreover, low roll compaction roll force correlated to a heterogeneous type of texture in the API chemical image. Overall, texture analysis of the tablets enabled efficient investigation of the spatial variation and could be used to advance process understanding. Finally, orthogonal projections to latent structures (O2PLS) model facilitated the understanding of the interrelationships between textural features, design parameters and physical properties data by separately joint and unique variations.

Support Vector Regression-based Multivariate Lesion Symptom Mapping

Support Vector Regression-based Multivariate Lesion Symptom Mapping

Support vector regression based multivariate lesion-symptom mapping.

A novel multivariate lesion-symptom mapping (LSM) methodology was developed in this study. Lesion analysis is a classic model for studying brain functions. Using lesion data, focal brain-behavior associations have been widely assessed using the massive voxel-based lesion symptom mapping (VLSM) method. Assessing each voxel independently, VLSM suffers from low sensitivity after correcting for the enormous number of comparisons. It is also incapable for assessing a spatially distributed association pattern though the brain-behavior associations generally involve a collection of functionally related voxels. To solve these two outstanding problems, we carried out the first multivariate lesion symptom mapping (MLSM) in this study using support vector regression (SVR). In the so dubbed SVR-LSM, the symptom relation to the entire lesion map rather than each isolated voxel is modeled using a non-linear function, so the inter-voxel correlations are intrinsically considered, resulting in a potentially more sensitive way to examine lesion-symptom relationships. Evaluations using synthetic data and real data showed that SVR-LSM gained a much better performance (in terms of sensitivity and specificity) for detecting brain-behavior relations than VLSM. While the method was designed for lesion analysis, extending it to neuroimaging data will be straightforward.

STANOVA: a smoothed-ANOVA-based model for spatio-temporal disease mapping

Spatio-temporal disease mapping can be viewed as a multivariate disease mapping problem with a given order of the geographic patterns to be studied. As a consequence, some of the techniques in multivariate literature could also be used to build spatio-temporal models. In this paper we propose using the smoothed ANOVA multivariate model for spatio-temporal problems. Under our approach the time trend for each geographic unit is modeled parametrically, projecting it on a preset orthogonal basis of functions (the contrasts in the smoothed ANOVA nomenclature), while the coefficients of these projections are considered to be spatially dependent random effects. Despite the parametric temporal nature of our proposal, we show with both simulated and real datasets that it may be as flexible as other spatio-temporal smoothing models proposed in the literature and may model spatio-temporal data with several sources of variability.

Exploring the rhetorical semiotic brand image structure of ad films with multivariate mapping techniques

The aim of this paper is to demonstrate the applicability of multivariate mapping techniques to the exploration of the rhetorical semiotic brand image structure of ad films. By drawing on correspondence analysis and multidimensional scaling, two techniques that are amply used in corpus linguistics and in marketing research, but also on the data reduction technique of factor analysis, it will be displayed how a set of nuclear semes and classemes or an intended semic structure that underlies ad filmic discursive structures may be projected alongside rhetorical figures by its internal stakeholders (i.e., a brand management team, an account planning team or a marketing research team) with view to attaining differential brand associations. The illustration of the exploratory analytical methods takes place by recourse to a corpus of 62 ad filmic texts from 13 subbrands of the 3 major brands in the UK cereals market and 321 ad filmic segments that resulted from the segmentation procedure. This paper seeks to contribute to advancements in the brand image and advertising rhetoric research streams by addressing distinctive modes of advertising rhetorical configuration at the level of the ad filmic text (as against print ads where the bulk of research on advertising rhetoric has concentrated), and moreover on a segmentbysegment level, rather than treating the ad film as a standalone unit of analysis, by adopting a multimodal outlook to advertising configurations that takes into account not only the verbal or the visual mode, but also interactions among modes, by adopting a product categoryspecific approach to advertising/brand textuality that draws on rhetorical semiotics and by employing exploratory statistical methods against the background of content analytic output.

Mapping Hedonic Data: A Process Perspective

Multivariate analyses are commonly used to study differences among items in a multidimensional space and to relate these findings to hedonic assessments of the same items. But there are numerous methods in use and the purpose of this article is to review these methods from a process standpoint. Specifically, this article considers the process assumptions behind several of the popular methods for multivariate mapping of hedonic data and argues that experimenters should consider how their data arise so that they can correctly interpret their findings. Among the methods considered in this article are models based on the hedonic continuum, internal and external preference mapping, and deterministic and probabilistic unfolding of preference and liking. Practical Applications Multivariate mapping of hedonic data has led to improved consumer products and a better understanding of consumer liking and choice. In this article, practitioners will find guidance in their choice of methods through a consideration of the processes that generate their data. Without a process-based perspective, practitioners will not be able to optimally interpret results from the wide variety of available multivariate mapping methods.

Investigating the single ideal assumption using Ideal Profile Method

Ideal point modeling is a type of multivariate mapping in which consumers are assumed to use internal ideals in their hedonic evaluation of products. In their calculation processes, these techniques typically assume that consumers have a unique ideal for the product set tested. But this assumption is difficult to verify from the liking data alone, and may be violated if different subcategories of products, such as light and dark chocolate, are included in the same experiment. In this paper we propose the use of the Ideal Profile Method (IPM) to test this assumption. In IPM, consumers are asked to rate explicitly their ideals for each product tested. The variability between products of the averaged ideal ratings can then be used to check for the assumption of a single ideal. The procedure we describe involves ANOVA and confidence ellipses associated to the Hotelling T2 test. We then consider three cases to illustrate the use of this methodology in practice.

Effective Graphic Features for Multivariate Symbol Mapping

Various patterns of cartographic multivariate symbols (MSs) have been developed to work efficiently with objective symbol mapping. One such MS pattern is composed of a graph that uses spatial data to represent statistical data. A preliminary study revealed major problems with users’ perceptions and understanding of the symbol. These problems present obstacles to the presentation objectives. We are therefore interested in studying the human factors that contribute to the visual perception of graph symbols. We predict that the graph symbols currently in use are too complex for viewers to fully understand what they are intended to represent. This research aims to study the mechanisms and behaviours of human visual perception that are necessary to understand cartography MSs and to determine a more effective prototype symbol that presents and conveys information to viewers more efficiently. The prototype design is based on a basic cartographic symbol and includes the characteristics and properties of the original patterns of graph symbols via a study of their pros and cons. Graphic structures and properties were modified to create a more effective symbol.

Q06 Multivariate classification of central nervous system-acting drugs based on in vivo response patterns – profile of pridopidine

BackgroundIn vivo pharmacological systems-response profiling, which uses a comprehensive array of biomarkers, was developed previously as a way to obtain a sensitive and detailed “fingerprint” of the functional effects of...

Dissecting anxiety-related QTLs in mice by univariate and multivariate mapping

To detect genes underlying anxiety-related traits in mice, we performed univariate and multivariate QTL mapping analyses of phenotypes obtained from 71 mice of the BXD recombinant inbred (RI) strains (n=528 mice) and their parental strains (C57BL/6J and DBA/2J). Separate and joint mapping analyses were carried out using a linkage map composed of 506 simple sequence repeats (SSRs). The main QTL effects, interactions between pairs of QTLs (epistasis), and their environmental interactions were estimated. The results showed that anxiety-related traits were influenced by multiple QTLs (five main effect QTLs and three epistatic QTLs). Ten potential anxiety-related candidate genes within the QTL intervals on chromosomes 5, 13 and 15 were identified. Some of these genes have been reported previously to be associated with the anxiety response. Based on our results, it is suggested that the multivariate QTL mapping approach improves the statistical power for detecting QTL and the precision of parameter estimation. Moreover, multivariate mapping can also detect pleiotropic QTL effects. multivariate QTL mapping, mouse, BXD strains, pleiotropy

The Neural Basis of Visual Word Form Processing: A Multivariate Investigation

Current research on the neurobiological bases of reading points to the privileged role of a ventral cortical network in visual word processing. However, the properties of this network and, in particular, its selectivity for orthographic stimuli such as words and pseudowords remain topics of significant debate. Here, we approached this issue from a novel perspective by applying pattern-based analyses to functional magnetic resonance imaging data. Specifically, we examined whether, where and how, orthographic stimuli elicit distinct patterns of activation in the human cortex. First, at the category level, multivariate mapping found extensive sensitivity throughout the ventral cortex for words relative to false-font strings. Secondly, at the identity level, the multi-voxel pattern classification provided direct evidence that different pseudowords are encoded by distinct neural patterns. Thirdly, a comparison of pseudoword and face identification revealed that both stimulus types exploit common neural resources within the ventral cortical network. These results provide novel evidence regarding the involvement of the left ventral cortex in orthographic stimulus processing and shed light on its selectivity and discriminability profile. In particular, our findings support the existence of sublexical orthographic representations within the left ventral cortex while arguing for the continuity of reading with other visual recognition skills.

Functional Mapping of Dynamic Traits with Robust t-Distribution

Functional mapping has been a powerful tool in mapping quantitative trait loci (QTL) underlying dynamic traits of agricultural or biomedical interest. In functional mapping, multivariate normality is often assumed for the underlying data distribution, partially due to the ease of parameter estimation. The normality assumption however could be easily violated in real applications due to various reasons such as heavy tails or extreme observations. Departure from normality has negative effect on testing power and inference for QTL identification. In this work, we relax the normality assumption and propose a robust multivariate -distribution mapping framework for QTL identification in functional mapping. Simulation studies show increased mapping power and precision with the distribution than that of a normal distribution. The utility of the method is demonstrated through a real data analysis.

Ring maps for spatial visualization of multivariate epidemiological data

Please click here to download the map associated with this article. Epidemiological research often involves the visual exploration of numerous attributes to help discern patterns between health and characteristics of the physical, socioeconomic, or built environment. Unfortunately, many of the multivariate mapping techniques discussed throughout the cartographic literature can be challenging to create or interpret—particularly for individuals without a cartographic background. In this paper, we present a new style of multivariate map—the ring map—to aid in basic visualization of multivariate datasets. For purposes of example, we focus on the use of the ring map style for exploring county-level epidemiological data for the state of South Carolina to examine patterns of age, race, or gender specific mortality and morbidity within a population.

Dietary Modulation of Gut Functional Ecology Studied by Fecal Metabonomics

A major source of intestinal metabolites results from both host and microbial processing of dietary nutrients. (1)H NMR-based metabolic profiling of mouse feces was carried out over time in different microbiome mouse models, including conventional (n = 9), conventionalized (n = 10), and "humanized" gnotobiotic mice inoculated with a model of human baby microbiota (HBM, n = 17). HBM mice were supplemented with Lactobacillus paracasei with (n = 10) and without (n = 7) prebiotics. Animals not supplemented with prebiotics received a diet enriched in glucose and lactose as placebo. In conventionalized animals, microbial populations and activities converged in term of multivariate mapping toward conventional mice. Both groups decreased bacterial processing of dietary proteins when switching to a diet enriched in glucose and lactose, as described with low levels of 5-aminovalerate, acetate, and propionate and high levels of lysine and arginine. The HBM model differs from conventional and conventionalized microbiota in terms of type, proportion, and metabolic activity of gut bacteria (lower short chain fatty acids (SCFAs), lactate, 5-aminovalerate, and oligosaccharides, higher bile acids and choline). The probiotics supplementation of HBM mice was associated with a specific amino acid pattern that can be linked to L. paracasei proteolytic activities. The combination of L. paracasei with the galactosyl-oligosaccharide prebiotics was related to the enhanced growth of bifidobacteria and lactobacilli, and a specific metabolism of carbohydrates, proteins, and SCFAs. The present study describes how the assessment of metabolic changes in feces may provide information for studying nutrient-microbiota relationships in different microbiome mouse models.

Bicomponent Trend Maps: A Multivariate Approach to Visualizing Geographic Time Series

The most straightforward approaches to temporal mapping cannot effectively illustrate all potentially significant aspects of spatio-temporal patterns across many regions and times. This paper introduces an alternative approach, bicomponent trend mapping, which employs a combination of principal component analysis and bivariate choropleth mapping to illustrate two distinct dimensions of long-term trend variations. The approach also employs a bicomponent trend matrix, a graphic that illustrates an array of typical trend types corresponding to different combinations of scores on two principal components. This matrix is useful not only as a legend for bicomponent trend maps but also as a general means of visualizing principal components. To demonstrate and assess the new approach, the paper focuses on the task of illustrating population trends from 1950 to 2000 in census tracts throughout major U.S. urban cores. In a single static display, bicomponent trend mapping is not able to depict as wide a variety of trend properties as some other multivariate mapping approaches, but it can make relationships among trend classes easier to interpret, and it offers some unique flexibility in classification that could be particularly useful in an interactive data exploration environment.

O processo de gestão de custos e planejamento de resultados utilizando técnicas de análise estatística de agrupamentos

This research aims to study the process of cost formation and the results of commercial agricultural activity by using the instruments provided by multivariate statistical analysis, dendrometric graphics and thematic mapping. The study was developed in an agricultural property located in the district of Cascavel, with a 434 ha cultivated area and divided into eight fields with varied dimensions. Two groups of variables were analyzed – one about costs and the other about the production results. According to the analysis, similarity levels were established both for the cost variables of cost and for the result variables. The multivariate cluster analysis is of great value, as it provides the conditions to directly analyze variables with different results as well as varied indexes without the need for extensive transformations and difficult assimilations. According to the results, it was concluded that cost management does not have any pattern parameters as in industry, as there is a different cost for each rural production unit, which should be managed in order to provide better results for its proprietary.

Optimal probability density function control for NARMAX stochastic systems

This paper presents a new control strategy for a class of non-Gaussian stochastic systems so that the output probability density function (PDF) of the system can be made to follow a desired PDF. The system considered is represented by an Nonlinear AutoRegressive and Moving Average with eXogenous (NARMAX) inputs with input channel time-delay and non-Gaussian noise. A multi-step-ahead nonlinear cumulative cost function is used to improve tracking performance. For this purpose, a relationship between the PDFs of all the inputs and the PDFs of multiple-step-ahead output is formulated by constructing an auxiliary multivariate mapping. By minimizing this performance function, a new explicit predictive controller design algorithm is established with less conservatism than some previous results. Furthermore, an improved approach is developed to guarantee the local stability of the closed-loop system by tuning the weighting parameters recursively. Simulations are given to demonstrate the effectiveness of the proposed control algorithm and desired results have been obtained.

Spatial‐scale partitioning of in situ turbulent flow data over a pebble cluster in a gravel‐bed river

Spatial structures within turbulent flow data were investigated through the use of a new multivariate variation partitioning analysis technique involving principal coordinates of neighbor matrices (PCNM), which is a form of distance‐based eigenvector maps (DBEM). The analysis revealed a significant (α = 0.01) spatial dependence, 58%, for the mean and turbulent flow variables. The flow variables were obtained from instantaneous two‐dimensional velocities collected in situ along a streamwise section that crosses over a pebble cluster in a gravel‐bed river. Using the orthogonal property of the PCNM variables, the explained variation was partitioned over four significant (α = 0.01) spatial scales: very large (VL, 17%), large (L, 24%), medium (M, 6%) and fine (F, 2%). Nearly 75% of the variance of the main turbulent flow indicators, such as the root‐mean‐square of the streamwise and vertical velocity components and the mean uv component Reynolds shear stress, was explained by the VL‐ and L‐scale PCNM submodels, which have streamwise and vertical length scales of the order of Δx = 5.3H − 2.6H and Δy = 1.0H − 0.5H (where H is the flow depth), respectively. Through a multivariate mapping procedure, clear spatial patterns within the explained flow variables emerge around the cluster, where the flow separation zone seems to play a significant role at a range of scales. As well, intervariable correlations at each spatial scale, obtained through eigenvector scatterplots, show intricate relationships between the flow variables. The interdependence of the Reynolds shear stress and the u component turbulent energy is much stronger at the VL scale than at the L and M scales. The application of PCNM analysis on the turbulent flow field shows the power of the technique to resolve the relevant spatial scales and patterns, and demonstrates its potential use in a variety of water resources studies.

Whitening and sweetening influences on filter coffee preference

As a basis for development of new, and rationalisation of existing products, consumer preferences in commercial filter coffees were examined in a quantitative approach with multivariate mapping. Coffees were brewed from 12 ground bean blends and served to 150 consumers as drunk normally – black, whitened and/or sweetened. In a complete block design with two sessions of six coffees, employing randomisation to reduce order effects, hedonic data on a five‐point scale were collected then processed using Q‐mode principal component analysis yielding preference maps for each presentation. Conventional descriptive profiling provided information on blend characters allowing soft modelling to determine sensory attributes driving preference scoring. From univariate data analyses, gender was a significant factor for presentation style: addition of milk increased preference scores for blends in males and sugar reduced preference ratings for females. The outcome was a series of consumer segmentations for different coffee presentations.