Connectionists: [Invitation] Workshop on Novel Applications of Dimensionality Reduction @ Fri Dec 8 - Fri Dec 8, 2006 (1 day)

[Kilian Weinberger]

connectionists at cs.cmu.edu, you are invited to

  Title: Workshop on Novel Applications of Dimensionality Reduction
  Time: Fri Dec 8 - Fri Dec 8, 2006 (1 day) (Eastern Time)
  Where: Whistler, CANADA
  Description: 
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                        Call For Papers
  
     Novel Applications of Dimensionality Reduction
   http://www.seas.upenn.edu/~kilianw/nldrworkshop06

          Workshop held at the 20th Annual Conference
            on Neural Information Processing Systems
                          (NIPS 2006)

              Whistler, CANADA: December 8, 2006
********************************************************************


  Dimensionality reduction is an important research topic in machine
  learning that is motivated by several needs, including
  data visualization and representation, discovering meaningful
  underlying structures, reducing computational complexity, and
  improving accuracy by avoiding overfitting due to data sparsity. In
  the past few years, significant progress has been made
  in this area, including development of novel algorithms for nonlinear
  dimensionality reduction (Isomap, locally linear embedding, local
  tangent space alignment, maximum variance unfolding,  etc.) and supervised
  dimensionality reduction (neighborhood components analysis, max-margin matrix
  factorization,  support vector decomposition, etc.) that have taken
  significant steps toward overcoming deficiencies of traditional
  (linear) methods like PCA and Fisher's LDA. At the same
  time, there is a growing interest in applying such novel
  dimensionality reduction techniques to a wide range of practical
  domains, including robotics, image driven navigation and localization,
  neuroscience, biomedical imaging, face recognition,
  bioinformatics and natural language processing, just to name a few.
  Typically, such applications have high-dimensional state- and
  action-spaces.
  Discovering low-dimensional structures in such spaces can improve our
  understanding of the domain, as well as the efficiency of learning
  and decision-making.

  Given these developments, we believe it is time to revisit the
  dimensionality reduction techniques from the point of view
  of various practical applications and their specific goals. The
  objective of this workshop is to understand how to match
  the capabilities of new nonlinear and supervised dimensionality
  reduction approaches with practical applications in science, engineering, and
  technology.
  We hope to achieve this by bringing together researchers who develop
  these techniques and those who apply them. A successful workshop will lead to new
  directions for application-oriented dimensionality-reduction research
  and ignite cross-fertilization between different application domains.


  This workshop will address the following questions:
  - Can we characterize which application domains are amenable to
  nonlinear and/or supervised dimensionality reduction?
  - Can we characterize which methods are best suited for specific
  application domains?
  - For a given application domain, what properties of the data must be
  preserved in the low-dimensional representation?
  - How should the application goal (such as prediction or
  decision-making) be
  used to influence the dimensionality reduction process?


  Suggested Topics
  =================
  We would welcome submissions on applications of dimensionality reduction (particularly, novel nonlinear and/or supervised methods) to various  practical  domains, including (but not limited to)  those mentioned above. We  encourage  case studies where such approaches improve the results (such as accuracy  or performance)
  or lead to better understanding of the underlying problem structure.
  We would also encourage analyses and comparisons of various approaches for specific  application scenarios.

  Format
  =======
  We are proposing a one-day workshop. We are planning on having one
  tutorial, 4 invited talks and shorter contributions from
  researches in industry and academia as well as a panel discussion.
  Each presentation will be followed by 10-15 minutes of
  discussion on the aspects detailed earlier in the overview. We will
  hold a poster session if we receive a sufficient number
  of good submissions. The workshop is intended to be accessible to the
  broader NIPS community and to encourage communication
  between different fields.

  Submission Instructions
  ========================

  We invite submissions of extended abstracts (up to 2 pages, not
  including bibliography) for the short contributed talks
  and/or posters. The submission should present a high-level
  description of recent or ongoing work related to the topics
  above. We will explore the possibility of publishing papers based on
  invited and submitted talks in a special issue of an
  appropriate journal.

  Email submissions to nips06workshop at watson.ibm.com as attachments in
  Postscript or PDF, no later than November 3, 2006.

  Information
  ============

      Workshop URL: http://www.seas.upenn.edu/~kilianw/nldrworkshop06

      Submission: nips06workshop at watson.ibm.com

      NIPS:  http://www.nips.cc

  Dates & Deadlines
  ==================

  November 3: Abstract Submission
  November 8: Acceptance Notification


  Organizing Committee
  =====================

  John Blitzer
  University of Pennsylvania, USA

  Rajarshi Das
  IBM T. J. Watson Research Lab, USA

  Irina Rish
  IBM T. J. Watson Research Lab, USA

  Kilian Weinberger (Chair)
  University of Pennsylvania, USA


  Invited Speakers
  =================
  TBA



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