High-Order Co-Clustering via Strictly Orthogonal and Symmetric L1-Norm Nonnegative Matrix Tri-Factorization
Kai Liu, Hua Wang
IJCAI - 2018
Different to traditional clustering methods that deal with one single type of data, High-Order Co-Clustering (HOCC) aims to cluster multiple types of data simultaneously by utilizing the interor/and intra-type relationships across different data types. In existing HOCC methods, data points routinely enter the objective functions with squared residual errors. As a result, outlying data samples can dominate the objective functions, which may lead to incorrect clustering results. Moreover, existing methods usually suffer from soft clustering, where the probabilities to different groups can be very close. In this paper, we propose an l1-norm symmetric nonnegative matrix tri-factorization method to solve the HOCC problem. Due to the orthogonal constraints and the symmetric l1-norm formulation in our new objective, conventional auxiliary function approach no longer works. Thus we derive the solution algorithm using the alternating direction method of multipliers. Extensive experiments have been conducted on a real world data set, in which promising empirical results, including less time consumption, strictly orthogonal membership matrix, lower local minima etc., have demonstrated the effectiveness of our proposed method.
Links
- View publications from Kai Liu
- View publications from Hua Wang
- View publications presented in IJCAI
- View publications in the project, Mining Brain Imaging Genomics Data for Improved Cognitive Health
- View publications in the project, Mining Materials Genome Data for Prediction and Guidance of Nanoparticle Synthesis
- View publications researching Matrix/Tensor Factorization
- View publications applied to Natural Language Processing
Cite this paper
MLA
Liu, Kai, and Hua Wang. "High-Order Co-Clustering via Strictly Orthogonal and Symmetric L1-Norm Nonnegative Matrix Tri-Factorization." IJCAI. 2018.
BibTeX
@inproceedings{liu2018high,
title={High-Order Co-Clustering via Strictly Orthogonal and Symmetric L1-Norm Nonnegative Matrix Tri-Factorization.},
author={Liu, Kai and Wang, Hua},
booktitle={IJCAI},
pages={2454--2460},
year={2018}
}