Paper Title:
Tensor Factorization and Clustering for the Feature Extraction Based on Tucker3 with Updating Core
  Abstract

Aiming at the problems of Tucker3 to large-scale tensor when applied to feature extraction, a new factorization based on Tucker3 is proposed to extract feature from the tensors. First, the large-scale tensor is divided into multiple sub-tensors so as to conveniently compute cores of sub-tensors in parallel mode with Matlab Parallel Computing Toolbox; Then, the cores of each sub-tensor are updated for reducing deviation in calculating and the similar characteristics of sub-tensors are clustered to obtain the features. Experiment results show that this methods is able to extract features rapidly and efficiently.

  Info
Periodical
Advanced Materials Research (Volumes 308-310)
Chapter
Other Related Topics
Edited by
Jian Gao
Pages
2517-2522
DOI
10.4028/www.scientific.net/AMR.308-310.2517
Citation
H. J. Wang, F. Y. Xu, F. Wang, "Tensor Factorization and Clustering for the Feature Extraction Based on Tucker3 with Updating Core", Advanced Materials Research, Vols. 308-310, pp. 2517-2522, 2011
Online since
August 2011
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Lin Cheng Jiang, Wen Tang Tan, Zhen Wen Wang, Feng Jing Yin, Bin Ge, Wen Dong Xiao
Chapter 4: Computational Methods and Algorithms, Applied Information Technologies
Abstract:Feature selection has become the focus of research areas of applications with high dimensional data. Nonnegative matrix factorization (NMF)...
2344