25 citations · 147 across the 36 of their papers we have counts for
15 papers · 1 filter
How smoothing the affinity matrix affects neighborhood preservation in t-SNE
Shirin Mohebi, Guillaume Bied, Jefrey Lijffijt
Dimensionality reduction methods are instrumental to visualize high-dimensional data, and t-SNE stands as one of the most widely used methods due to its emphasis on local neighborh…
BiMi Sheets: Infosheets for bias mitigation methods
MaryBeth Defrance, Guillaume Bied, Maarten Buyl +2
Over the past 15 years, hundreds of bias mitigation methods have been proposed in the pursuit of fairness in machine learning (ML). However, algorithmic biases are domain-, task-,…
InfoClus: Informative Clustering of High-dimensional Data Embeddings
Fuyin Lai, Edith Heiter, Guillaume Bied +1
Developing an understanding of high-dimensional data can be facilitated by visualizing that data using dimensionality reduction. However, the low-dimensional embeddings are often d…
Gaussian Embedding of Temporal Networks
Raphaël Romero, Jefrey Lijffijt, Riccardo Rastelli +2
Representing the nodes of continuous-time temporal graphs in a low-dimensional latent space has wide-ranging applications, from prediction to visualization. Yet, analyzing continuo…
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…
A Systematic Evaluation of Node Embedding Robustness
Alexandru Mara, Jefrey Lijffijt, Stephan Günnemann +1
Node embedding methods map network nodes to low dimensional vectors that can be subsequently used in a variety of downstream prediction tasks. The popularity of these methods has g…
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…
Topologically Regularized Data Embeddings
Robin Vandaele, Bo Kang, Jefrey Lijffijt +2
Unsupervised feature learning often finds low-dimensional embeddings that capture the structure of complex data. For tasks for which prior expert topological knowledge is available…
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…