Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12476
Title: Tensor distance based multilinear globality preserving embedding : a unified tensor based dimensionality reduction framework for image and video classification
Authors: Liu, Y
Liu, Y 
Zhong, S
Chan, KCC 
Keywords: Image and video classification
Multilinear globality preserving embedding strategy
Multimedia mining
Pattern recognition
Tensor based learning
Tensor distance based multilinear isometric embedding
Tensor distance based multilinear multidimensional scaling
Tensor distance metric
Issue Date: 2012
Publisher: Pergamon Press
Source: Expert systems with applications, 2012, v. 39, no. 12, p. 10500-10511 How to cite?
Journal: Expert systems with applications 
Abstract: Image and video classification tasks often suffer from the problem of high-dimensional feature space. How to discover the meaningful, low-dimensional representations of such high-order, high-dimensional observations remains a fundamental challenge. In this paper, we present a unified framework for tensor based dimensionality reduction including a new tensor distance (TD) metric and a novel multilinear globality preserving embedding (MGPE) strategy. Different with the traditional Euclidean distance, which is constrained by orthogonality assumption, TD measures the distance between data points by considering the relationships among different coordinates of high-order data. To preserve the natural tensor structure in low-dimensional space, MGPE directly works on the high-order form of input data and employs an iterative strategy to learn the transformation matrices. To provide faithful global representation for datasets, MGPE intends to preserve the distances between all pairs of data points. According to the proposed TD metric and MGPE strategy, we further derive two algorithms dubbed tensor distance based multilinear multidimensional scaling (TD-MMDS) and tensor distance based multilinear isometric embedding (TD-MIE). TD-MMDS finds the transformation matrices by keeping the TDs between all pairs of input data in the embedded space, while TD-MIE intends to preserve all pairwise distances calculated according to TDs along shortest paths in the neighborhood graph. By integrating tensor distance into tensor based embedding, TD-MMDS and TD-MIE perform tensor based dimensionality reduction through the whole learning procedure and achieve obvious performance improvement on various standard datasets.
URI: http://hdl.handle.net/10397/12476
ISSN: 0957-4174
EISSN: 1873-6793
DOI: 10.1016/j.eswa.2012.02.139
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

1
Last Week
0
Last month
0
Citations as of Aug 14, 2017

WEB OF SCIENCETM
Citations

1
Last Week
0
Last month
0
Citations as of Aug 13, 2017

Page view(s)

43
Last Week
0
Last month
Checked on Aug 13, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.