CALL FOR PARTICIPATION - NIPS workshop on novelty detection

[Thomas Petsche]

There will be a NIPS workshop on 

	Novelty detection and adaptive system monitoring

which will focus on novelty detection, unsupervised learning,
and algorithms designed to monitor a system to detect failures or
incorrect behavior.  A more detailed description is attached 
below.

If you would be interested in making a presentation at this workshop,
please send email to
	petsche at scr.siemens.com

We are interested in presentations on 
* novelty detection and unsupervised learning algorithms;
* models of biological novelty detection and unsupervised learning
  systems;
* real-world examples of monitoring or novelty detection problems --
  whether you have a final solution yet or not.

TITLE
Novelty detection and adaptive system monitoring

DESCRIPTION
The purpose of the discussion is to bring together researchers working
on different real world system monitoring tasks and those working on novelty
detection algorithms and models in order to hasten the development of broadly
applicable adaptive monitoring algorithms.

Unexpected failure of a machine or system can have severe and
expensive consequences.  One of the most infamous examples is the
sudden failure of military helicopter rotor gearboxes, which lead to a
complete loss of the helicopter and all aboard.  There are many, more
mundane, similar examples.  The unexpected failure of a motor in a
paper mill causes a loss of the product in production as well as lost
production time while the motor is replaced.  A computer or network
overload, due to normal traffic or a virus invasion, can lead to a
system crash that can cause loss of data and downtime.

In these examples and others, it can be cost effective to ``monitor''
the system of interest and signal an operator when the monitored
conditions indicate an imminent failure.  Thus, one might assign a
technician to listen to all the fire pumps on a ship and replace any
that starts to sound like it is in danger of failing.  This is
analogous to periodically glancing at the fuel gauge in your car to
make sure you do not run out of gas.

An adaptive system monitor is an adaptive that estimates the condition
of the system from a set of periodic measurements.  This task is
typically complicated by the fact that the measurements are complex
and high dimensional.  Adaptation is necessary since the measurements
will depend on the peculiarities of the system being monitored and its
environment.

This workshop will focus on the use of novelty detection for the
problem of system monitoring.  A novelty detector is a device or
algorithm which is trained on a set of examples and learns to
recognize or reproduce those examples.  Any new example that is
significantly different from the training set is identified as
``novel'' because it is unlike any example in the training set.

We will discuss various approaches to novelty detection; how it
differs from multiple class supervised learning and purely
unsupervised learning; biological relevance and how to use what is
know about biological systems; complexity issues due to single class
data; how to detect only certain types of novelty; and the use of
novelty detection algorithms on real world devices such as helicopter
rotor gears, electric motors, computers,  networks, automobiles etc.



FORMAT
We aim to have presentations about real world monitoring problems,
novelty detection and monitoring algorithms, and biological and
psychological models that exhibit novelty detection all aimed to stir up
questions and discussions.


WORKSHOP CHAIRS
Thomas Petsche and Stephen J. Hanson
Siemens Corporate Research, Inc.

Mark Gluck
Rutgers University


VERY BRIEF RESUMES

Thomas Petsche leads a 2 year-old effort to develop a electric motor
monitoring system.  

Stephen J. Hanson is the head of the Learning Systems Department at
SCR and a frequent contributor to the motor monitoring project.

Mark Gluck is a professor of neurobiology at Rutgers University and
has authored several papers on a model of the hippocampus based on a
neural network auto-associator which functions as a novelty detector.