Visual Exploration of High‐Dimensional Data through Subspace Analysis and Dynamic Projections

Liu, S.; Wang, B.; Thiagarajan, Jayaraman J.; Bremer, Peer-Timo; Pascucci, Valerio

doi:10.1111/cgf.12639

Cited by 40 publications

(24 citation statements)

References 36 publications

(78 reference statements)

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“…Thus, the selection of the dataset and the choice of the dimensions of the projection plane affect the outcome of PCA or PCoA remarkably. Refined methods have been proposed to visualize multidimensional data, which facilitate the search for a suitable projection plane …”

Section: Discussionmentioning

confidence: 99%

Navigating within thiamine diphosphate‐dependent decarboxylases: Sequences, structures, functional positions, and binding sites

et al. 2019

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Thiamine diphosphate‐dependent decarboxylases catalyze both cleavage and formation of CC bonds in various reactions, which have been assigned to different homologous sequence families. This work compares 53 ThDP‐dependent decarboxylases with known crystal structures. Both sequence and structural information were analyzed synergistically and data were analyzed for global and local properties by means of statistical approaches (principle component analysis and principal coordinate analysis) enabling complexity reduction. The different results obtained both locally and globally, that is, individual positions compared with the overall protein sequence or structure, revealed challenges in the assignment of separated homologous families. The methods applied herein support the comparison of enzyme families and the identification of functionally relevant positions. The findings for the family of ThDP‐dependent decarboxylases underline that global sequence identity alone is not sufficient to distinguish enzyme function. Instead, local sequence similarity, defined by comparisons of structurally equivalent positions, allows for a better navigation within several groups of homologous enzymes. The differentiation between homologous sequences is further enhanced by taking structural information into account, such as BioGPS analysis of the active site properties or pairwise structural superimpositions. The methods applied herein are expected to be transferrable to other enzyme families, to facilitate family assignments for homologous protein sequences.

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Section: Discussionmentioning

confidence: 99%

Navigating within thiamine diphosphate‐dependent decarboxylases: Sequences, structures, functional positions, and binding sites

et al. 2019

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“…Sedlmair et al [SHB∗14] provide a comprehensive survey of visual analytics tools for analyzing the parameter space of models. Example types of models used by these visual analytics tools include regression [MP13], clustering [NHM∗07, CD19, KEV∗18, SKB∗18], classification [VDEvW11, CLKP10], dimension reduction [CLL∗13, JZF∗09, NM13, AWD12, LWT∗15], and domain‐specific modeling approaches including climate models [WLSL17]. In these examples, the user directly constructs or modifies the parameters of the model through the interaction of sliders or interactive visual elements within the visualization.…”

Section: Related Workmentioning

confidence: 99%

A User‐based Visual Analytics Workflow for Exploratory Model Analysis

Cashman

Humayoun

Heimerl

et al. 2019

Computer Graphics Forum

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Many visual analytics systems allow users to interact with machine learning models towards the goals of data exploration and insight generation on a given dataset. However, in some situations, insights may be less important than the production of an accurate predictive model for future use. In that case, users are more interested in generating of diverse and robust predictive models, verifying their performance on holdout data, and selecting the most suitable model for their usage scenario. In this paper, we consider the concept of Exploratory Model Analysis (EMA), which is defined as the process of discovering and selecting relevant models that can be used to make predictions on a data source. We delineate the differences between EMA and the well‐known term exploratory data analysis in terms of the desired outcome of the analytic process: insights into the data or a set of deployable models. The contributions of this work are a visual analytics system workflow for EMA, a user study, and two use cases validating the effectiveness of the workflow. We found that our system workflow enabled users to generate complex models, to assess them for various qualities, and to select the most relevant model for their task.

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“…Other systems such as [44] provide analysts with drop-down menus to select a distance metric. Further options are to let the analyst determine interesting features in combination with subspace clustering (e.g., [41]) or quality metrics (e.g., [31]). S4 Feature Selection & Emphasis was the most frequently implemented interaction scenario (37).…”

Section: S4 Feature Selection and Emphasismentioning

confidence: 99%

Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis

Sacha

Zhang

Sedlmair

et al. 2017

IEEE Trans. Visual. Comput. Graphics

202

139

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Abstract-Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data. For DR techniques to be useful in exploratory data analysis, they need to be adapted to human needs and domain-specific problems, ideally, interactively, and on-the-fly. Many visual analytics systems have already demonstrated the benefits of tightly integrating DR with interactive visualizations. Nevertheless, a general, structured understanding of this integration is missing. To address this, we systematically studied the visual analytics and visualization literature to investigate how analysts interact with automatic DR techniques. The results reveal seven common interaction scenarios that are amenable to interactive control such as specifying algorithmic constraints, selecting relevant features, or choosing among several DR algorithms. We investigate specific implementations of visual analysis systems integrating DR, and analyze ways that other machine learning methods have been combined with DR. Summarizing the results in a "human in the loop" process model provides a general lens for the evaluation of visual interactive DR systems. We apply the proposed model to study and classify several systems previously described in the literature, and to derive future research opportunities.

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Visual Exploration of High‐Dimensional Data through Subspace Analysis and Dynamic Projections

Cited by 40 publications

References 36 publications

Navigating within thiamine diphosphate‐dependent decarboxylases: Sequences, structures, functional positions, and binding sites

Navigating within thiamine diphosphate‐dependent decarboxylases: Sequences, structures, functional positions, and binding sites

A User‐based Visual Analytics Workflow for Exploratory Model Analysis

Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis

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