Robust Multi-Relational Clustering via L1-Norm Symmetric Nonnegative Matrix Factorization
Kai Liu, Hua Wang
ACL - 2015
In this paper, we propose an l1-norm Symmetric Nonnegative Matrix Tri-Factorization (l1 S-NMTF) framework to cluster multi-type relational data by utilizing their interrelatedness. Due to introducing the l1-norm distances in our new objective function, the proposed approach is robust against noise and outliers, which are inherent in multi-relational data. We also derive the solution algorithm and rigorously analyze its correctness and convergence. The promising experimental results of the algorithm applied to text clustering on IMDB dataset validate the proposed approach.
Links
- View publications from Kai Liu
- View publications from Hua Wang
- View publications presented in ACL
- View publications researching Matrix/Tensor Factorization
- View publications researching Robust Learning Models
- View publications applied to Natural Language Processing
Cite this paper
MLA
Liu, Kai, and Hua Wang. "Robust Multi-Relational Clustering via ℓ1-Norm Symmetric Nonnegative Matrix Factorization-Norm Symmetric Nonnegative Matrix Factorization." Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). 2015.
BibTeX
@inproceedings{liu2015robust,
title={Robust Multi-Relational Clustering via ℓ1-Norm Symmetric Nonnegative Matrix Factorization-Norm Symmetric Nonnegative Matrix Factorization},
author={Liu, Kai and Wang, Hua},
booktitle={Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
pages={397--401},
year={2015}
}