## Robust Novelty Detection with Single-Class MPM

Gert Lanckriet

(Professors Laurent El Ghaoui and Michael I. Jordan)

(NSF) IIS-9988642 and (ONR) MURI N00014-00-1-0637

In this project we consider the problem of novelty detection,
presenting an algorithm that aims to find a minimal region in
input space containing a fraction alpha of the probability
mass underlying a data set. This algorithm, the "single-class
minimax probability machine (MPM)," is built on a distribution-free
methodology that minimizes the worst-case probability of a data point
falling outside of a convex set, given only the mean and covariance matrix
of the distribution and making no further distributional assumptions.
We present a robust approach to estimating the mean and covariance
matrix within the general two-class MPM setting, and show how this
approach specializes to the single-class problem. We provide empirical
results comparing the single-class MPM to the single-class SVM and a
two-class SVM method.

More information (http://robotics.eecs.berkeley.edu/~gert/) or

Send mail to the author : (gert@eecs.berkeley.edu)

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