Semi-Supervised Clustering via Structural Entropy with Different Constraints
Semi-supervised clustering techniques have emerged as valuable tools for leveraging prior
information in the form of constraints to improve the quality of clustering outcomes. Despite …
information in the form of constraints to improve the quality of clustering outcomes. Despite …
Exemplar-based Continual Learning via Contrastive Learning
Despite the impressive performance of deep learning models, they suffer from catastrophic
forgetting, which refers to a significant decline in overall performance when trained with new …
forgetting, which refers to a significant decline in overall performance when trained with new …
Hypergraph-based convex semi-supervised unconstraint symmetric matrix factorization for image clustering
W Luo, Z Wu, N Zhou - Information Sciences, 2024 - Elsevier
Semi-supervised symmetric nonnegative matrix factorization (SNMF) has been extensively
utilized in both linear and nonlinear data clustering tasks. However, the current SNMF …
utilized in both linear and nonlinear data clustering tasks. However, the current SNMF …
Locality sensitive hashing scheme based on online-learning
J Zhang, Y Yang, Y Liu - Journal of Visual Communication and Image …, 2024 - Elsevier
Abstract Locally Sensitive Hashing (LSH) algorithms are classical algorithms commonly
used on the c-Approximate Nearest Neighbor (c-ANN) search problem. When using …
used on the c-Approximate Nearest Neighbor (c-ANN) search problem. When using …
Constrained Propagation Self-Adaptived Semi-Supervised Non-Negative Matrix Factorization Clustering Algorithm.
ZHU Tuoji, LIN Haoshen, Z Weihao… - Journal of …, 2024 - search.ebscohost.com
Symmetric non-negative matrix factorization (NMF) can naturally capture the embedded
clustering structure in the graph representation. It is an important method for linear and …
clustering structure in the graph representation. It is an important method for linear and …