Name
Abstract | ||
|---|---|---|
Dimension reduction techniques are essential for feature selection and feature extraction of complex high-dimensional data. These techniques, which construct low-dimensional representations of data, are typically geometrically motivated, computationally efficient and approximately preserve certain structural properties of the data. However, they are often used as black box solutions in data exploration and their results can be difficult to interpret. To assess the quality of these results, quality measures, such as co-ranking [LV09], have been proposed to quantify structural distortions that occur between high-dimensional and low-dimensional data representations. Such measures could be evaluated and visualized point-wise to further highlight erroneous regions [MLGH13]. In this work, we provide an interactive visualization framework for exploring high-dimensional data via its two-dimensional embeddings obtained from dimension reduction, using a rich set of user interactions. We ask the following question: what new insights do we obtain regarding the structure of the data, with interactive manipulations of its embeddings in the visual space? We augment the two-dimensional embeddings with structural abstractions obtained from hierarchical clusterings, to help users navigate and manipulate subsets of the data. We use point-wise distortion measures to highlight interesting regions in the domain, and further to guide our selection of the appropriate level of clusterings that are aligned with the regions of interest. Under the static setting, point-wise distortions indicate the level of structural uncertainty within the embeddings. Under the dynamic setting, on-the-fly updates of point-wise distortions due to data movement and data deletion reflect structural relations among different parts of the data, which may lead to new and valuable insights. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1111/cgf.12366 | Comput. Graph. Forum |
Field | DocType | Volume |
Data mining,Dimensionality reduction,Feature selection,Computer science,Theoretical computer science,Artificial intelligence,Distortion,Black box (phreaking),Visual space,Computer vision,Clustering high-dimensional data,Feature extraction,Interactive visualization,Machine learning | Journal | 33 |
Issue | ISSN | Citations |
3 | 0167-7055 | 17 |
PageRank | References | Authors |
0.58 | 24 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shusen Liu | 1 | 46 | 1.96 |
| Bei Wang | 2 | 528 | 61.48 |
| Peer-Timo Bremer | 3 | 1446 | 82.47 |
| Valerio Pascucci | 4 | 3241 | 192.33 |