Robust joint and individual variance explained
File(s)
Accepted version
Author(s)
Sagonas, C
Panagakis, Y
Leidinger, A
Zafeiriou, S
Type
Conference Paper
Abstract
Discovering the common (joint) and individual sub-
spaces is crucial for analysis of multiple data sets, including
multi-view and multi-modal data. Several statistical ma-
chine learning methods have been developed for discover-
ing the common features across multiple data sets. The most
well studied family of the methods is that of Canonical Cor-
relation Analysis (CCA) and its variants. Even though the
CCA is a powerful tool, it has several drawbacks that ren-
der its application challenging for computer vision appli-
cations. That is, it discovers only common features and not
individual ones, and it is sensitive to gross errors present
in visual data. Recently, efforts have been made in order
to develop methods that discover individual and common
components. Nevertheless, these methods are mainly appli-
cable in two sets of data. In this paper, we investigate the
use of a recently proposed statistical method, the so-called
Joint and Individual Variance Explained (JIVE) method, for
the recovery of joint and individual components in an arbi-
trary number of data sets. Since, the JIVE is not robust to
gross errors, we propose alternatives, which are both robust
to non-Gaussian noise of large magnitude, as well as able
to automatically find the rank of the individual components.
We demonstrate the effectiveness of the proposed approach
to two computer vision applications, namely facial expres-
sion synthesis and face age progression in-the-wild.
spaces is crucial for analysis of multiple data sets, including
multi-view and multi-modal data. Several statistical ma-
chine learning methods have been developed for discover-
ing the common features across multiple data sets. The most
well studied family of the methods is that of Canonical Cor-
relation Analysis (CCA) and its variants. Even though the
CCA is a powerful tool, it has several drawbacks that ren-
der its application challenging for computer vision appli-
cations. That is, it discovers only common features and not
individual ones, and it is sensitive to gross errors present
in visual data. Recently, efforts have been made in order
to develop methods that discover individual and common
components. Nevertheless, these methods are mainly appli-
cable in two sets of data. In this paper, we investigate the
use of a recently proposed statistical method, the so-called
Joint and Individual Variance Explained (JIVE) method, for
the recovery of joint and individual components in an arbi-
trary number of data sets. Since, the JIVE is not robust to
gross errors, we propose alternatives, which are both robust
to non-Gaussian noise of large magnitude, as well as able
to automatically find the rank of the individual components.
We demonstrate the effectiveness of the proposed approach
to two computer vision applications, namely facial expres-
sion synthesis and face age progression in-the-wild.
Date Issued
2017-11-09
Date Acceptance
2017-03-03
Citation
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp.5739-5748
Publisher
IEEE
Start Page
5739
End Page
5748
Journal / Book Title
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Copyright Statement
© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Commission of the European Communities
Engineering & Physical Science Research Council (E
Grant Number
688520
EP/N007743/1
Source
2017 IEEE International Conference on Computer Vision and Pattern Recognition
Subjects
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
Computer Science
Engineering
Publication Status
Published
Start Date
2017-07-21
Finish Date
2017-07-26
Coverage Spatial
Hawaii, USA
Date Publish Online
2017-11-09