What to measure next to improve decision making? On top-down task driven feature saliency

Lars Kai Hansen, Seliz Karadogan, Letizia Marchegiani

AbstractTop-down attention is modeled as decision making
based on incomplete information. We consider decisions made
in a sequential measurement situation where initially only an
incomplete input feature vector is available, however, where we
are given the possibility to acquire additional input values among
the missing features. The procecure thus poses the question
what to do next? We take an information theoretical approach
implemented for generality in a generative mixture model. The
framework allows us reduce the decision about what to measure
next in a classification problem to the estimation of a few onedimensional
integrals per missing feature. We demonstrate the
viability of the framework on four well-known classification
problems.
TypeConference paper [With referee]
Year2011    Month April
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing


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