@CONFERENCE\{IMM2011-06102,
    author       = "S. G. Karadogan and L. Marchegiani and J. Larsen and L. K. Hansen",
    title        = "Top-Down Attention with Features Missing at Random",
    year         = "2011",
    month        = "sep",
    keywords     = "Machine learning, missing data techniques, attention modeling, entropy",
    booktitle    = "International Workshop on Machine Learning for Signal Processing",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "{IEEE} Press",
    organization = "",
    address      = "",
    url          = "http://ieeexplore.ieee.org/search/searchresult.jsp?newsearch=true&queryText=karadogan+larsen&filter=&.x=0&.y=0&tag=1",
    abstract     = "In this paper we present a top-down attention model designed for
an environment in which features are missing completely at random.
Following (Hansen et al., 2011) we model top-down attention as a
sequential decision making process driven by a task - modeled as a
classification problem - in an environment with random subsets of
features missing, but where we have the possibility to gather additional
features among the ones that are missing. Thus, the top-down
attention problem is reduced to finding the answer to the question
what to measure next? Attention is based on the top-down saliency
of the missing features given as the estimated difference in classification
confusion (entropy) with and without the given feature. The difference
in confusion is computed conditioned on the available set of
features. In this work, we make our attention model more realistic by
also allowing the initial training phase to take place with incomplete
data. Thus, we expand the model to include a missing data technique
in the learning process. The top-down attention mechanism
is implemented in a Gaussian Discrete mixture model setting where
marginals and conditionals are relatively easy to compute. To illustrate
the viability of expanded model, we train the mixture model
with two different datasets, a synthetic data set and the well-known
Yeast dataset of the {UCI} database. We evaluate the new algorithm in
environments characterized by different amounts of incompleteness
and compare the performance with a system that decides next feature
to be measured at random. The proposed top-down mechanism
clearly outperforms random choice of the next feature.",
    isbn_issn    = "{DOI} 0.1109/MLSP.2011.6064577"
}