Steerable, Progressive Multidimensional Scaling

Abstract

Current implementations of Multidimensional Scaling (MDS), an approach that attempts to best represent data point similarity in a low-dimensional representation, are not suited for many of today's large-scale datasets. We propose an extension to the spring model approach that allows the user to interactively explore datasets that are far beyond the scale of previous implementations of MDS.We present MDSteer, a steerable MDS computation engine and visualization tool that progressively computes an MDS layout and handles datasets of over one million points. Our technique employs hierarchical data structures and progressive layouts to allow the user to steer the computation of the algorithm to the interesting areas of the dataset. The algorithm iteratively alternates between a layout stage in which a sub-selection of points are added to the set of active points affected by the MDS iteration, and a binning stage which increases the depth of the bin hierarchy and organizes the currently unplaced points into separate spatial regions. This binning strategy allows the user to select onscreen regions of the layout to focus the MDS computation into the areas of the dataset that are assigned to the selected bins. We show both real and common synthetic benchmark datasets with dimensionalities ranging from 3 to 300 and cardinalities of over one million points.

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“…Other cognitive traits too have also been shown to have a strong influence on users' performance. The study by Conati and Maclaren [7] looked at two different visualizations to represent changes in a set of variables: a radar graph and a Multiscale Dimension Visualizer (MDV); a visualization that primarily uses color hue and intensity to represent change direction and magnitude [23]. They found that: (1) a user's perceptual speed was a significant predictor of which of the two visualizations would work better for that user on a specific comparison task, and (2) both perceptual speed and visual spatial working memory were predictors of performance with each visualization for some of the study's tasks.…”

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“…As algorithms get more and more complex and tend to carry black-box characteristics, interpreting an algorithmic outcome is increasingly gaining importance. Recent examples by the Google Big Picture team 4 or promising innovative publications such as Distill.pub 5 are demonstrating how an effective use of visualization can help unravel algorithms and make them more accessible for wider audiences and also help in educational purposes. However, examples so far are often designed case-by-case basis and further research is needed to develop guidelines, best-practices and a systematic characterization of the role and scope of visualization and interaction.…”

“…The field of visualization, and in particular visual analytics, is a discipline that is motivated by these complex problems that require a concerted effort from computational methods and the human analyst to be addressed [3]. Expert users have the domain knowledge to steer algorithmic power to where it is needed the most [4], and offer the capability and creativity to fine-tune computational results and turn them into data-informed decisions [5]. As a growing field, there are already several effective examples where the combination of visualisation and machine learning are being developed to offer novel solutions for data-intensive problems [1].…”

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“…[51], [52], [53], [54], [55], [56] Groups & Classification [57] [58], [59] [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74] [75], [76], [77], [78], [79], [80] Dependence & Prediction [81], [82], [46] [83], [84], [85], [86], [87], [88], [89] [90], [91], [92] being analyzed. The results are then presented to the user through different visual encodings that are often accompanied by interaction.…”

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