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Self-Representative Manifold Concept Factorization with Adaptive Neighbors for Clustering
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Matrix Factorization based methods, e.g., the Concept Factorization (CF) and Nonnegative Matrix Factorization (NMF), have been proved to be efficient and effective for data clustering tasks. In recent years, various graph extensions of CF and NMF have been proposed to explore intrinsic geometrical structure of data for the purpose of better clustering performance. However, many methods build the affinity matrix used in the manifold structure directly based on the input data. Therefore, the
doi:10.24963/ijcai.2018/352
dblp:conf/ijcai/MaZHZWL18
fatcat:h7kbcamq6zfi7csoaud7cevcgq