Semi-supervised constrained clustering: An in-depth overview, ranked taxonomy and future research directions
G González-Almagro, D Peralta, E De Poorter… - ar** discrete sets of instances with similar characteristics. Constrained …
Semantic trajectory representation and retrieval via hierarchical embedding
Trajectory mining has gained growing attention due to its emerging applications, such as
location-based services, urban computing, and movement behavior analyses. One critical …
location-based services, urban computing, and movement behavior analyses. One critical …
Ssne: Effective node representation for link prediction in sparse networks
MR Chen, P Huang, Y Lin, SM Cai - IEEE access, 2021 - ieeexplore.ieee.org
Graph embedding is gaining popularity for link prediction in complex networks. However,
few works focus on the effectiveness of graph embedding models on link prediction in …
few works focus on the effectiveness of graph embedding models on link prediction in …
Robust semi-supervised non-negative matrix factorization with structured normalization
L Wang, N Guan, D Shi, Z Fan, L Su - IEEE Access, 2019 - ieeexplore.ieee.org
Non-negative matrix factorization (NMF) approximates a non-negative data matrix with the
product of two low-rank non-negative matrices by minimizing the cost of such approximation …
product of two low-rank non-negative matrices by minimizing the cost of such approximation …