# Exploring Multi-dimensional Data via Subset Embedding

arXiv: https://arxiv.org/abs/2104.11867 · HTML: https://mlsanity.com/paper/2104.11867/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 70 references that are in the map.
- First posted on arXiv 24 Apr 2021.

## Builds on (most cited first, 14 of 14)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [Convolutional Two-Stream Network Fusion for Video Action Recognition](https://mlsanity.com/paper/1604.06573/) (2016)
- [A Survey on Multi-view Learning](https://mlsanity.com/paper/1304.5634/) (2013)
- [A Survey of Multi-View Representation Learning](https://mlsanity.com/paper/1610.01206/) (2016)
- [t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections](https://mlsanity.com/paper/2002.06910/) (2020)
- [Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning](https://mlsanity.com/paper/1905.03911/) (2019)
- [DimReader: Axis lines that explain non-linear projections](https://mlsanity.com/paper/1710.00992/) (2017)
- [A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction](https://mlsanity.com/paper/2008.01645/) (2020)
- [An Incremental Dimensionality Reduction Method for Visualizing Streaming Multidimensional Data](https://mlsanity.com/paper/1905.04000/) (2019)
- [The Subspace Voyager: Exploring High-Dimensional Data along a Continuum of Salient 3D Subspaces](https://mlsanity.com/paper/1603.04781/) (2016)
- [Visual Analysis of High-Dimensional Event Sequence Data via Dynamic Hierarchical Aggregation](https://mlsanity.com/paper/1906.07617/) (2019)
- [Selection Bias Tracking and Detailed Subset Comparison for High-Dimensional Data](https://mlsanity.com/paper/1906.07625/) (2019)
- [Interactive dimensionality reduction using similarity projections](https://mlsanity.com/paper/1811.05531/) (2018)

## Built on by (most cited first, 1 of 1)

- [Improved Evaluation and Generation of Grid Layouts using Distance Preservation Quality and Linear Assignment Sorting](https://mlsanity.com/paper/2205.04255/) (2022)

## Abstract (arXiv)

Multi-dimensional data exploration is a classic research topic in visualization. Most existing approaches are designed for identifying record patterns in dimensional space or subspace. In this paper, we propose a visual analytics approach to exploring subset patterns. The core of the approach is a subset embedding network (SEN) that represents a group of subsets as uniformly-formatted embeddings. We implement the SEN as multiple subnets with separate loss functions. The design enables to handle arbitrary subsets and capture the similarity of subsets on single features, thus achieving accurate pattern exploration, which in most cases is searching for subsets having similar values on few features. Moreover, each subnet is a fully-connected neural network with one hidden layer. The simple structure brings high training efficiency. We integrate the SEN into a visualization system that achieves a 3-step workflow. Specifically, analysts (1) partition the given dataset into subsets, (2) select portions in a projected latent space created using the SEN, and (3) determine the existence of patterns within selected subsets. Generally, the system combines visualizations, interactions, automatic methods, and quantitative measures to balance the exploration flexibility and operation efficiency, and improve the interpretability and faithfulness of the identified patterns. Case studies and quantitative experiments on multiple open datasets demonstrate the general applicability and effectiveness of our approach.