23 citations · 89 across the 24 of their papers we have counts for
10 papers · 1 filter
Revised Conditional t-SNE: Looking Beyond the Nearest Neighbors
Edith Heiter, Bo Kang, Ruth Seurinck +1
Conditional t-SNE (ct-SNE) is a recent extension to t-SNE that allows removal of known cluster information from the embedding, to obtain a visualization revealing structure beyond…
Topologically Regularized Data Embeddings
Edith Heiter, Robin Vandaele, Tijl De Bie +2
Unsupervised representation learning methods are widely used for gaining insight into high-dimensional, unstructured, or structured data. In some cases, users may have prior topolo…
SimHawNet: A Modified Hawkes Process for Temporal Network Simulation
Mathilde Perez, Raphaël Romero, Bo Kang +3
Temporal networks allow representing connections between objects while incorporating the temporal dimension. While static network models can capture unchanging topological regulari…
ExClus: Explainable Clustering on Low-dimensional Data Representations
Xander Vankwikelberge, Bo Kang, Edith Heiter +1
Dimensionality reduction and clustering techniques are frequently used to analyze complex data sets, but their results are often not easy to interpret. We consider how to support u…
The Curse Revisited: When are Distances Informative for the Ground Truth in Noisy High-Dimensional Data?
Robin Vandaele, Bo Kang, Tijl De Bie +1
Distances between data points are widely used in machine learning applications. Yet, when corrupted by noise, these distances -- and thus the models based upon them -- may lose the…
FONDUE: A Framework for Node Disambiguation Using Network Embeddings
Ahmad Mel, Bo Kang, Jefrey Lijffijt +1
Real-world data often presents itself in the form of a network. Examples include social networks, citation networks, biological networks, and knowledge graphs. In their simplest fo…
ALPINE: Active Link Prediction using Network Embedding
Xi Chen, Bo Kang, Jefrey Lijffijt +1
Many real-world problems can be formalized as predicting links in a partially observed network. Examples include Facebook friendship suggestions, consumer-product recommendations,…
Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information
Bo Kang, Darío García García, Jefrey Lijffijt +2
Dimensionality reduction and manifold learning methods such as t-Distributed Stochastic Neighbor Embedding (t-SNE) are routinely used to map high-dimensional data into a 2-dimensio…
ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions
Bo Kang, Jefrey Lijffijt, Tijl De Bie
Networks are powerful data structures, but are challenging to work with for conventional machine learning methods. Network Embedding (NE) methods attempt to resolve this by learnin…
Informative Data Projections: A Framework and Two Examples
Tijl De Bie, Jefrey Lijffijt, Raul Santos-Rodriguez +1
Methods for Projection Pursuit aim to facilitate the visual exploration of high-dimensional data by identifying interesting low-dimensional projections. A major challenge is the de…