Sign in
Author

Conference

Journal

Organization

Year

DOI
Look for results that meet for the following criteria:
since
equal to
before
between
and
Search in all fields of study
Limit my searches in the following fields of study
Agriculture Science
Arts & Humanities
Biology
Chemistry
Computer Science
Economics & Business
Engineering
Environmental Sciences
Geosciences
Material Science
Mathematics
Medicine
Physics
Social Science
Multidisciplinary
Keywords
(7)
Dimensional Reduction
High Dimensional Data
High Dimensionality
Image Segmentation
Semi Supervised Learning
Side Information
Spectral Clustering
Related Publications
(1)
Learning Effective Image Metrics from Few Pairwise Examples
Subscribe
Academic
Publications
SemiSupervised Dimensionality Reduction Using Pairwise Equivalence Constraints
SemiSupervised Dimensionality Reduction Using Pairwise Equivalence Constraints,Hakan Cevikalp,Jakob J. Verbeek,Frédéric Jurie,Alexander Kläser
Edit
SemiSupervised Dimensionality Reduction Using Pairwise Equivalence Constraints
(
Citations: 9
)
BibTex

RIS

RefWorks
Download
Hakan Cevikalp
,
Jakob J. Verbeek
,
Frédéric Jurie
,
Alexander Kläser
To deal with the problem of insufficient labeled data, usually
side information
 given in the form of pairwise equivalence constraints between points  is used to discover groups within data. However, existing methods using
side information
typically fail in cases with highdimensional spaces. In this paper, we address the prob lem of learning from
side information
for highdimensional data. To this end, we propose a semisupervised dimensionality reduction scheme that incorporates pairwise equivalence constraints for finding a better embed ding space, which improves the performance of subsequent clustering and classification phases. Our method builds on the assumption that points in a sufficiently small neighborhood tend to have the same label. Equiv alence constraints are employed to modify the neighborhoods and to increase the separability of different classes. Experimental results on highdimensional image data sets show that integrating
side information
into the dimensionality reduction improves the clustering and classification performance.
Conference:
Computer Vision Theory and Applications  VISAPP
, pp. 489496, 2008
Cumulative
Annual
View Publication
The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.
(
lear.inrialpes.fr
)
(
www.informatik.unitrier.de
)
Citation Context
(7)
...For instance, faces extracted in successive video frames in roughly same location can be assumed as same person, whereas faces extracted in the same frame in different locations can be seen as different persons [
2
]...
...Cevikalp et al. [
2
] proposed pairwise constrained locality preserv� ing projections (CLPP)...
GX Yu
,
et al.
Mixture graph based semisupervised dimensionality reduction
...To this end, [10] and [
3
] proposed semisupervised dimensionality reduction methods that exploit both constraints and unconstrained data...
Su Yan
,
et al.
DSP: Robust Semisupervised Dimensionality Reduction Using Dual Subspa...
...Previously relevant methods [5] [6] [
7
] [8] implicitly assume that a class consists of a single cluster...
...CLPP [
7
] builds an affinity matrix, each entry of which indicates the similarity between two points...
Bin Tong
,
et al.
SubclassOriented Dimension Reduction with Constraint Transformation a...
...Traditional answers include preservation of maximum variance as in principal component analysis (PCA); preservation of an independence structure as in independent component analysis; preservation of distances and pairwise constraints as in [
1
]; or maximization of class predictive power as in linear discriminant analysis, informative discriminant analysis [2], neighborhood components analysis [3], metric learning by collapsing classes [4], ...
Jaakko Peltonen
.
Visualization by Linear Projections as Information Retrieval
...Learning distance metrics is very important for various vision applications such as object classification, image retrieval, and video retrieval (Chen et al., 2005;
Cevikalp et al., 2008;
Hertz et al., 2003; Hadsell et al., 2006), and this task is much easier when the target values (labels) associated to the data samples are available...
...Furthermore, learning an effective full rank distance metric by using side information cannot be carried out in such highdimensional spaces since the number of parameters to be estimated is related to the square of the dimensionality and there is insufficient side information to obtain accurate estimates (
Cevikalp et al., 2008
)...
...A better approach would be to use a semisupervised dimensionality reduction method to find a lowdimensional embedding satisfying the pairwise equivalence constraints as in (
Cevikalp et al., 2008
)...
...The proposed method bears similarity to the semisupervised dimension reduction method introduced in (
Cevikalp et al., 2008
), but it does not assume that samples in a sufficiently small neighborhood tend to have same label...
...The proposed SemiSupervised Distance Metric Learning (SSDML) algorithm is compared to the full rank distance metric learning algorithm followed by dimensionality reduction and the Constrained Locality Preserving Projection (CLPP) method of (
Cevikalp et al., 2008
)...
...As in (
Cevikalp et al., 2008
), we set the number of clusters to two, one cluster for the background and another for the object of interest...
Hakan Cevikalp
,
et al.
Semisupervised Distance Metric Learning for Visual Object Classificat...
References
(20)
Active SemiSupervision for Pairwise Constrained Clustering
(
Citations: 119
)
Sugato Basu
,
Arindam Banerjee
,
Raymond J. Mooney
Conference:
SIAM International Conference on Data Mining  SDM
, 2004
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
(
Citations: 616
)
Mikhail Belkin
,
Partha Niyogi
Conference:
Neural Information Processing Systems  NIPS
, pp. 585591, 2001
Integrating constraints and metric learning in semisupervised clustering
(
Citations: 213
)
Mikhail Bilenko
,
Sugato Basu
,
Raymond J. Mooney
Conference:
International Conference on Machine Learning  ICML
, 2004
Enhancing Image and Video Retrieval: Learning via Equivalence Constraint
(
Citations: 24
)
Tomer Hertz
,
Noam Shental
,
Aharon Barhillel
,
Daphna Weinshall
Conference:
Computer Vision and Pattern Recognition  CVPR
, pp. 668674, 2003
A Maximum Entropy Framework for PartBased Texture and Object Recognition
(
Citations: 66
)
Svetlana Lazebnik
,
Cordelia Schmid
,
Jean Ponce
Conference:
International Conference on Computer Vision  ICCV
, vol. 1, pp. 832838, 2005
Sort by:
Citations
(9)
Mixture graph based semisupervised dimensionality reduction
(
Citations: 1
)
GX Yu
,
H. Peng
,
J. Wei
,
Q. L. Ma
Published in 2010.
DSP: Robust Semisupervised Dimensionality Reduction Using Dual Subspace Projections
Su Yan
,
Sofien Bouaziz
,
Dongwon Lee
Conference:
Web Intelligence  WI
, pp. 398405, 2010
SubclassOriented Dimension Reduction with Constraint Transformation and Manifold Regularization
Bin Tong
,
Einoshin Suzuki
Conference:
PacificAsia Conference on Knowledge Discovery and Data Mining  PAKDD
, pp. 113, 2010
Visualization by Linear Projections as Information Retrieval
(
Citations: 1
)
Jaakko Peltonen
Published in 2009.
Semisupervised Distance Metric Learning for Visual Object Classification
(
Citations: 1
)
Hakan Cevikalp
,
Roberto Paredes
Conference:
Computer Vision Theory and Applications  VISAPP
, pp. 315322, 2009