Dynamical projections for the visualization of PDFSense data

A recent paper on visualizing the sensitivity of hadronic experiments to nucleon structure [1] introduces the tool PDFSense which defines measures to allow the user to judge the sensitivity of PDF fits to a given experiment. The sensitivity is characterized by high-dimensional data residuals that are visualized in a 3-d subspace of the 10 first principal components or using t-SNE [2]. We show how a tour, a dynamic visualisation of high dimensional data, can extend this tool beyond 3-d relationships. This approach enables resolving structure orthogonal to the 2-d viewing plane used so far, and hence finer tuned assessment of the sensitivity.Comment: Format of the animations changed for easier viewin

Coordinating views for data visualisation and algorithmic profiling

A number of researchers have designed visualisation systems that consist of multiple components, through which data and interaction commands flow. Such multistage (hybrid) models can be used to reduce algorithmic complexity, and to open up intermediate stages of algorithms for inspection and steering. In this paper, we present work on aiding the developer and the user of such algorithms through the application of interactive visualisation techniques. We present a set of tools designed to profile the performance of other visualisation components, and provide further functionality for the exploration of high dimensional data sets. Case studies are provided, illustrating the application of the profiling modules to a number of data sets. Through this work we are exploring ways in which techniques traditionally used to prepare for visualisation runs, and to retrospectively analyse them, can find new uses within the context of a multi-component visualisation system

Visual and computational analysis of structure-activity relationships in high-throughput screening data

Novel analytic methods are required to assimilate the large volumes of structural and bioassay data generated by combinatorial chemistry and high-throughput screening programmes in the pharmaceutical and agrochemical industries. This paper reviews recent work in visualisation and data mining that can be used to develop structure-activity relationships from such chemical/biological datasets

Evaluating Interactive Visualization of Multidimensional Data Projection with Feature Transformation

There has been extensive research on dimensionality reduction techniques. While these make it possible to present visually the high-dimensional data in 2D or 3D, it remains a challenge for users to make sense of such projected data. Recently, interactive techniques, such as Feature Transformation, have been introduced to address this. This paper describes a user study that was designed to understand how the feature transformation techniques affect user’s understanding of multi-dimensional data visualisation. It was compared with the traditional dimension reduction techniques, both unsupervised (PCA) and supervised (MCML). Thirty-one participants were recruited to detect visual clusters and outliers using visualisations produced by these techniques. Six different datasets with a range of dimensionality and data size were used in the experiment. Five of these are benchmark datasets, which makes it possible to compare with other studies using the same datasets. Both task accuracy and completion time were recorded for comparison. The results show that there is a strong case for the feature transformation technique. Participants performed best with the visualisations produced with high-level feature transformation, in terms of both accuracy and completion time. The improvements over other techniques are substantial, particularly in the case of the accuracy of the clustering task. However, visualising data with very high dimensionality (i.e., greater than 100 dimensions) remains a challenge

Visualization as a method for relationship discovery in data Halina

Visualization techniques are especially relevant to multidimensional data, the analysis of which is limited by human perception abilities. The paper presents a hybrid method of multidimensional data analysis. The main goal was to test the efficiency of the method in the context of real-life medical data. A short survey of issues and techniques concerned with data visualization are also included

Feed-forward neural networks and topographic mappings for exploratory data analysis

A recent novel approach to the visualisation and analysis of datasets, and one which is particularly applicable to those of a high dimension, is discussed in the context of real applications. A feed-forward neural network is utilised to effect a topographic, structure-preserving, dimension-reducing transformation of the data, with an additional facility to incorporate different degrees of associated subjective information. The properties of this transformation are illustrated on synthetic and real datasets, including the 1992 UK Research Assessment Exercise for funding in higher education. The method is compared and contrasted to established techniques for feature extraction, and related to topographic mappings, the Sammon projection and the statistical field of multidimensional scaling

Evaluating interactive visualization of multidimensional data projection with feature transformation

There has been extensive research on dimensionality reduction techniques. While these make it possible to present visually the high-dimensional data in 2D or 3D, it remains a challenge for users to make sense of such projected data. Recently, interactive techniques, such as Feature Transformation, have been introduced to address this. This paper describes an user study that was designed to understand how the feature transformation techniques affect user’s understanding of multi-dimensional data visualisation. It was compared with the traditional dimension reduction techniques, both unsupervised (PCA) and supervised (MCML). Thirty-one participants were recruited to detect visually clusters and outliers using visualisations produced by these techniques. Six different datasets with a range of dimensionality and data size were used in the experiment. Five of these are benchmark datasets, which makes it possible to compare with other studies using the same datasets. Both task accuracy and completion time were recorded for comparison. The results showthat there is a strong case for the feature transformation technique. Participants performed best with the visualisations produced with high-level feature transformation, in terms of both accuracy and completion time. The improvements over other techniques are substantial, particularly in the case of the accuracy of the clustering task. However, visualising data with very high dimensionality (i.e., greater than 100 dimensions) remains a challenge

Which solar EUV indices are best for reconstructing the solar EUV irradiance ?

The solar EUV irradiance is of key importance for space weather. Most of the time, however, surrogate quantities such as EUV indices have to be used by lack of continuous and spectrally resolved measurements of the irradiance. The ability of such proxies to reproduce the irradiance from different solar atmospheric layers is usually investigated by comparing patterns of temporal correlations. We consider instead a statistical approach. The TIMED/SEE experiment, which has been continuously operating since Feb. 2002, allows for the first time to compare in a statistical manner the EUV spectral irradiance to five EUV proxies: the sunspot number, the f10.7, Ca K, and Mg II indices, and the He I equivalent width. Using multivariate statistical methods such as multidimensional scaling, we represent in a single graph the measure of relatedness between these indices and various strong spectral lines. The ability of each index to reproduce the EUV irradiance is discussed; it is shown why so few lines can be effectively reconstructed from them. All indices exhibit comparable performance, apart from the sunspot number, which is the least appropriate. No single index can satisfactorily describe both the level of variability on time scales beyond 27 days, and relative changes of irradiance on shorter time scales.Comment: 6 figures, to appear in Adv. Space. Re

Visualising interactions in bi- and triadditive models for three-way tables

This paper concerns the visualisation of interaction in three-way arrays. It extends some standard ways of visualising biadditive modelling for two-way data to the case of three-way data. Three-way interaction is modelled by the Parafac method as applied to interaction arrays that have main effects and biadditive terms removed. These interactions are visualised in three and two dimensions. We introduce some ideas to reduce visual overload that can occur when the data array has many entries. Details are given on the interpretation of a novel way of representing rank-three interactions accurately in two dimensions. The discussion has implications regarding interpreting the concept of interaction in three-way arrays