A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration
Dimensionality reduction is a common method for analyzing and visualizing high-dimensional data. However, reasoning dynamically about the results of a dimensionality reduction is difficult. Dimensionality-reduction algorithms use complex optimizations to reduce the number of dimensions of a dataset, but these new dimensions often lack a clear relation to the initial data dimensions, thus making them difficult to interpret. Here we propose a visual interaction framework to improve dimensionality-reduction based exploratory data analysis. We introduce two interaction techniques, forward projection and backward projection, for dynamically reasoning about dimensionally reduced data. We also contribute two visualization techniques, prolines and feasibility maps, to facilitate the effective use of the proposed interactions. We apply our framework to PCA and autoencoder-based dimensionality reductions. Through data-exploration examples, we demonstrate how our visual interactions can improve the use of dimensionality reduction in exploratory data analysis.
Code (0)
등록된 구현이 없습니다.
Tasks
Dimensionality ReductionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Review, Framework and R toolkit for Exploring, Evaluating, and Comparing Visualizations
This paper gives a review and synthesis of methods of evaluating dimensionality reduction techniques. Particular attention is paid to rank-order neighborhood evaluation metrics. A framework is created for exploring dimen…
Dimensionality ReductionRiemannian joint dimensionality reduction and dictionary learning on symmetric positive definite manifold
Dictionary leaning (DL) and dimensionality reduction (DR) are powerful tools to analyze high-dimensional noisy signals. This paper presents a proposal of a novel Riemannian joint dimensionality reduction and dictionary l…
ClassificationDictionary LearningDimensionality ReductionGeneral Classification+3Dimensionality Reduction Considered Harmful (Some of the Time)
Visual analytics now plays a central role in decision-making across diverse disciplines, but it can be unreliable: the knowledge or insights derived from the analysis may not accurately reflect the underlying data. In th…
Dimensionality ReductionNonlinear Dimensionality Reduction on Graphs
In this era of data deluge, many signal processing and machine learning tasks are faced with high-dimensional datasets, including images, videos, as well as time series generated from social, commercial and brain network…
Dimensionality ReductionTime SeriesTime Series AnalysisSpectral Overlap and a Comparison of Parameter-Free, Dimensionality Reduction Quality Metrics
Nonlinear dimensionality reduction methods are a popular tool for data scientists and researchers to visualize complex, high dimensional data. However, while these methods continue to improve and grow in number, it is of…
Dimensionality ReductionHyperparameter Optimization