Accessibility navigation


Dimensionality reduction assisted tensor clustering

Sun, Y., Gao, J., Hong, X., Guo, Y. and Harris, C. J. (2014) Dimensionality reduction assisted tensor clustering. In: 2014 International Joint Conference on Neural Networks (IJCNN), July 6-11, 2014, Beijing, China.

Full text not archived in this repository.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

Official URL: http://dx.doi.org/10.1109/IJCNN.2014.6889385

Abstract/Summary

This paper is concerned with tensor clustering with the assistance of dimensionality reduction approaches. A class of formulation for tensor clustering is introduced based on tensor Tucker decomposition models. In this formulation, an extra tensor mode is formed by a collection of tensors of the same dimensions and then used to assist a Tucker decomposition in order to achieve data dimensionality reduction. We design two types of clustering models for the tensors: PCA Tensor Clustering model and Non-negative Tensor Clustering model, by utilizing different regularizations. The tensor clustering can thus be solved by the optimization method based on the alternative coordinate scheme. Interestingly, our experiments show that the proposed models yield comparable or even better performance compared to most recent clustering algorithms based on matrix factorization.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:39728
Uncontrolled Keywords:Tensor Tucker Decomposition, Tensor Clustering, Matrix Factorization, Tensor PCA.

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation