Title: Sparse mean localization by information theory,
Abstract: Sparse feature selection is necessary when we fit statistical models, we have
access to a large group of features, don't know which are relevant, but assume
that most are not. Alternatively, when the number of features is larger than
the available data the model becomes over parametrized and the sparse feature
selection task involves selecting the most informative variables for the model.
When the model is a simple location model and the number of relevant features
does not grow with the total number of features, sparse feature selection
corresponds to sparse mean estimation. We deal with a simplified mean
estimation problem consisting of an additive model with gaussian noise and mean
that is in a restricted, finite hypothesis space. This restriction simplifies
the mean estimation problem into a selection problem of combinatorial nature.
Although the hypothesis space is finite, its size is exponential in the
dimension of the mean. In limited data settings and when the size of the
hypothesis space depends on the amount of data or on the dimension of the data,
choosing an approximation set of hypotheses is a desirable approach. Choosing a
set of hypotheses instead of a single one implies replacing the bias-variance
trade off with a resolution-stability trade off. Generalization capacity
provides a resolution selection criterion based on allowing the learning
algorithm to communicate the largest amount of information in the data to the
learner without error. In this work the theory of approximation set coding and
generalization capacity is explored in order to understand this approach. We
then apply the generalization capacity criterion to the simplified sparse mean
estimation problem and detail an importance sampling algorithm which at once
solves the difficulty posed by large hypothesis spaces and the slow convergence
of uniform sampling algorithms.