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cs.LG2024
Scalable Multi-view Clustering via Explicit Kernel Features Maps
Chakib Fettal, Lazhar Labiod, Mohamed Nadif
The proliferation of high-dimensional data from sources such as social media, sensor networks, and online platforms has created new challenges for clustering algorithms. Multi-view…
cs.LG2024
Graph Cuts with Arbitrary Size Constraints Through Optimal Transport
Chakib Fettal, Lazhar Labiod, Mohamed Nadif
A common way of partitioning graphs is through minimum cuts. One drawback of classical minimum cut methods is that they tend to produce small groups, which is why more balanced var…
cs.LG2019
Spectral Clustering via Ensemble Deep Autoencoder Learning (SC-EDAE)
Severine Affeldt, Lazhar Labiod, Mohamed Nadif
Recently, a number of works have studied clustering strategies that combine classical clustering algorithms and deep learning methods. These approaches follow either a sequential w…