24th, 2009] Workshop program is posted online.
Confirmed Invited Speaker: Prof.
Sanjay Ranka (University
of Florida). Talk title: Novel Mixture Models to Learn Complex
Patterns in High-Dimensional Data.
10th, 2009] Confirmed Invited Speaker: Prof.
Chris Ding (University of Texas at
Arlington). Talk title: Tensor
Decompositions: New Clustering Capability and Error Bounds.
[October 20, 2009]
The list of accepted papers is listed below:
- Chengying Miao. An Effective Network Partitioning Algorithm
Based on Two-Point Diffusing Strategy
- Dan Zhang and Luo Si.
Multiple Instance Transfer Learning.
- Pritam Chanda, Aidong Zhang and Murali Ramanathan.
Attribute Interactions Using Information Theoretic Metrics.
- Shuo Chen, Bin Liu, Mingjie Qian and Changshui Zhang.
K-means Based Framework for Aggregate Outputs Classification.
- Ke Tang and Rui Wang.
Feature Selection for Maximizing the Area
Under the ROC Curve.
- Jana Nononicova, Petr Somol and Pavel Pudi.
A New Stability
Measure for Feature Selection Algorithms.
- Hongliang Fei, Brian Quanz and Jun Huan. GLSVM: Integrating
Structured Feature Selection and Large Margin Classification.
- Mingjie Qian, Feiping Nie and Changshui Zhang.
Labeled Semi-supervised SVM.
- Kunal Punera and Suju Rajan.
Improving Multilabel Classification
in Hierarchical Taxonomies.
- Gregory Moore, Charles Bergeron and Kristin Bennett.
Bilevel Programming for Hyperparameter Selection.
1st, 2009] PC members added
16th, 2009] Webpage Kickoff
Classical optimization techniques have found
widespread use in solving traditional data mining problems, among which
convex optimization has occupied
the center-stage because of its elegant property of global optimum. Many
problems can be casted into the convex optimization framework, such as Support
Vector Machines, graph-based manifold learning, and clustering, which can
usually be solved by convex Quadratic Programming, Semi-Definite Programming or
As time goes by, new problems emerge
constantly in data mining community, such as Time-Evolving Data Mining,
On-Line Data Mining, Relational Data Mining and Transferred Data Mining. While
at the same time fundamental problems such as classification and clustering
continue to be better understand. Some of these
recently emerged problems are more complex than traditional ones and are usually
formulated as nonconvex problems. Therefore some general optimization methods,
such as gradient descents, coordinate descents, convex relaxation, have come
back to the stage and become more and more popular in recent years.
This workshop will
present recent advances in optimization techniques for, especially new emerging,
data mining problems, as well as the real-life
applications among this community. One main goal of the workshop is to bring
together leading researchers who work on state-of-the-art algorithms on
optimization based methods for modern data analysis, and also the practitioners
who seek for novel applications. In summary, this workshop
will strive to emphasize the following aspects:
recent advances in algorithms and methods using optimization techniques
- Addressing the
fundamental challenges in data mining using optimization techniques
killer applications and key industry drivers (where theories and
interactions among researchers (from different backgrounds) sharing the same
interest to promote cross-fertilization of ideas.
benchmark data for better evaluation of the techniques
areas for the workshop include (but are not limited to) the following:
Methods and algorithms:
Component Analysis and Singular value decomposition for clustering and
learning (classification, semi-supervised learning and unsupervised
kernel/structured kernel learning
- Large margin
- Large scale
algorithms, compressive sensing
- Genomics and
Bioinformatics by fusing different information sources
- Information search
and extraction from Web using different domain knowledge
- Scientific computing
and computational sciences
- Sensor network
- Social information
retrieval by fusing different information sources
- Social Networks
- Text processing
and information retrieval
- Image processing
computing and computational sciences
- July 17th, 2009: Electronic submission of full papers
- September 8th, 2009: Author notification
- September 28th, 2009: Submission of Camera-ready papers
- December 6th, 2009: Workshop in
Miami Beach, Florida, USA.
|Program Committee Members
University of California, Davis
Microsoft Research Asia
Heng Huang, University of
Texas at Arlington
University of California at Berkeley
James Kwok, Hongkong
University of Science and Technology
Tsinghua University, China
Dacheng Tao, Nanyang
Technological University, Singapore
Fei Sha, University of
Vikas Sindhwani, IBM
T. J. Watson Research Lab
Tokyo Institute of Technology
Sun, IBM T. J. Watson Research Lab
Yangqiu Song, IBM Research
Gang Wang, Microsoft
Linli Xu, University of
National University of Singapore
Kai Zhang, Lawrence
Berkeley National Lab