InVis: A Tool for Interactive Visual Data Analysis

Paurat, Daniel; Gärtner, Thomas

doi:10.1007/978-3-642-40994-3_52

Cited by 7 publications

(9 citation statements)

References 12 publications

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“…In this paper we extend our previous work on interactive visualization in which we proposed a variant of supervised PCA [19] and provided a tool for interactive data visualization InVis [18]. In contrast to supervised PCA [19] which allowed interaction with a visualization only through the explicit placement of control points, the proposed knowledge-based kernel PCA allows interaction through a variety of soft and/or hard knowledge-based constraints (see Section 3.1, 3.2 or 3.3).…”

Section: Related Workmentioning

confidence: 93%

“…The InVis tool, on the other hand, enables interaction with a visualization using the least square projections (LSP). As argued in our workshop paper [19], the LSP algorithm is in general not a good choice for data visualization and the same can be said about any purely supervised learning algorithm (e.g. linear discriminant analysis [13]).…”

Section: Related Workmentioning

confidence: 95%

“…In contrast to supervised PCA [19] which allowed interaction with a visualization only through the explicit placement of control points, the proposed knowledge-based kernel PCA allows interaction through a variety of soft and/or hard knowledge-based constraints (see Section 3.1, 3.2 or 3.3). Moreover, we relate the successive optimization problems arising during the interaction with rank-one updates and reduce the interaction complexity from cubic to quadratic in the number of data instances.…”

Section: Related Workmentioning

confidence: 97%

“…The alternative way is to treat the placements as hard constraints [19] and incorporate them into Υ term which can be written as…”

Section: The Placement Of Control Pointsmentioning

confidence: 99%

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Interactive Knowledge-Based Kernel PCA

Oglic

Paurat

Gärtner

2014

Machine Learning and Knowledge Discovery in Databases

Self Cite

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Abstract. Data understanding is an iterative process in which domain experts combine their knowledge with the data at hand to explore and confirm hypotheses. One important set of tools for exploring hypotheses about data are visualizations. Often, however, traditional, unsupervised dimensionality reduction algorithms are used for visualization. These tools allow for interaction, i.e., exploring different visualizations, only by means of manipulating some technical parameters of the algorithm. Therefore, instead of being able to intuitively interact with the visualization, domain experts have to learn and argue about these technical parameters. In this paper we propose a knowledge-based kernel PCA approach that allows for intuitive interaction with data visualizations. Each embedding direction is given by a non-convex quadratic optimization problem over an ellipsoid and has a globally optimal solution in the kernel feature space. A solution can be found in polynomial time using the algorithm presented in this paper. To facilitate direct feedback, i.e., updating the whole embedding with a sufficiently high frame-rate during interaction, we reduce the computational complexity further by incremental up-and down-dating. Our empirical evaluation demonstrates the flexibility and utility of this approach.

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Section: Related Workmentioning

confidence: 93%

Section: Related Workmentioning

confidence: 95%

Section: Related Workmentioning

confidence: 97%

“…The alternative way is to treat the placements as hard constraints [19] and incorporate them into Υ term which can be written as…”

Section: The Placement Of Control Pointsmentioning

confidence: 99%

See 2 more Smart Citations

Interactive Knowledge-Based Kernel PCA

Oglic

Paurat

Gärtner

2014

Machine Learning and Knowledge Discovery in Databases

Self Cite

View full text Add to dashboard Cite

show abstract

“…The few tools that exist are either designed for specific problems and domains (e.g., itemset and subgroup discovery [1,4,7], information retrieval [10], or analysis of networks [2]) and/or aim to present information that align with the user's beliefs (e.g., semi-supervised PCA [7]). However, users are typically interested in finding structures in the data that contrast with their current knowledge [5].…”

Section: Introductionmentioning

confidence: 99%

A Tool for Subjective and Interactive Visual Data Exploration

Kang

Puolamäki

Lijffijt

et al. 2016

Machine Learning and Knowledge Discovery in Databases

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Abstract. We present SIDE, a tool for Subjective and Interactive Visual Data Exploration, which lets users explore high dimensional data via subjectively informative 2D data visualizations. Many existing visual analytics tools are either restricted to specific problems and domains or they aim to find visualizations that align with user's belief about the data. In contrast, our generic tool computes data visualizations that are surprising given a user's current understanding of the data. The user's belief state is represented as a set of projection tiles. Hence, this user-awareness o↵ers users an e cient way to interactively explore yet-unknown features of complex high dimensional datasets.

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SICA: subjectively interesting component analysis

Kang

Lijffijt

Santos-Rodríguez

et al. 2018

Data Min Knowl Disc

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The information in high-dimensional datasets is often too complex for human users to perceive directly. Hence, it may be helpful to use dimensionality reduction methods to construct lower dimensional representations that can be visualized. The natural question that arises is how do we construct a most informative low dimensional representation? We study this question from an information-theoretic perspective and introduce a new method for linear dimensionality reduction. The obtained model that quantifies the informativeness also allows us to flexibly account for prior knowledge a user may have about the data. This enables us to provide representations that are subjectively interesting. We title the method Subjectively Interesting Component Analysis (SICA) and expect it is mainly useful for iterative data mining. SICA Responsible editor: Fei Wang.

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InVis: A Tool for Interactive Visual Data Analysis

Cited by 7 publications

References 12 publications

Interactive Knowledge-Based Kernel PCA

Interactive Knowledge-Based Kernel PCA

A Tool for Subjective and Interactive Visual Data Exploration

SICA: subjectively interesting component analysis

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