42,342 research outputs found
The color of smiling: computational synaesthesia of facial expressions
This note gives a preliminary account of the transcoding or rechanneling
problem between different stimuli as it is of interest for the natural
interaction or affective computing fields. By the consideration of a simple
example, namely the color response of an affective lamp to a sensed facial
expression, we frame the problem within an information- theoretic perspective.
A full justification in terms of the Information Bottleneck principle promotes
a latent affective space, hitherto surmised as an appealing and intuitive
solution, as a suitable mediator between the different stimuli.Comment: Submitted to: 18th International Conference on Image Analysis and
Processing (ICIAP 2015), 7-11 September 2015, Genova, Ital
Unsupervised Learning via Total Correlation Explanation
Learning by children and animals occurs effortlessly and largely without
obvious supervision. Successes in automating supervised learning have not
translated to the more ambiguous realm of unsupervised learning where goals and
labels are not provided. Barlow (1961) suggested that the signal that brains
leverage for unsupervised learning is dependence, or redundancy, in the sensory
environment. Dependence can be characterized using the information-theoretic
multivariate mutual information measure called total correlation. The principle
of Total Cor-relation Ex-planation (CorEx) is to learn representations of data
that "explain" as much dependence in the data as possible. We review some
manifestations of this principle along with successes in unsupervised learning
problems across diverse domains including human behavior, biology, and
language.Comment: Invited contribution for IJCAI 2017 Early Career Spotlight. 5 pages,
1 figur
Objective Classification of Galaxy Spectra using the Information Bottleneck Method
A new method for classification of galaxy spectra is presented, based on a
recently introduced information theoretical principle, the `Information
Bottleneck'. For any desired number of classes, galaxies are classified such
that the information content about the spectra is maximally preserved. The
result is classes of galaxies with similar spectra, where the similarity is
determined via a measure of information. We apply our method to approximately
6000 galaxy spectra from the ongoing 2dF redshift survey, and a mock-2dF
catalogue produced by a Cold Dark Matter-based semi-analytic model of galaxy
formation. We find a good match between the mean spectra of the classes found
in the data and in the models. For the mock catalogue, we find that the classes
produced by our algorithm form an intuitively sensible sequence in terms of
physical properties such as colour, star formation activity, morphology, and
internal velocity dispersion. We also show the correlation of the classes with
the projections resulting from a Principal Component Analysis.Comment: submitted to MNRAS, 17 pages, Latex, with 14 figures embedde
The information bottleneck method
We define the relevant information in a signal as being the
information that this signal provides about another signal y\in \Y. Examples
include the information that face images provide about the names of the people
portrayed, or the information that speech sounds provide about the words
spoken. Understanding the signal requires more than just predicting , it
also requires specifying which features of \X play a role in the prediction.
We formalize this problem as that of finding a short code for \X that
preserves the maximum information about \Y. That is, we squeeze the
information that \X provides about \Y through a `bottleneck' formed by a
limited set of codewords \tX. This constrained optimization problem can be
seen as a generalization of rate distortion theory in which the distortion
measure d(x,\x) emerges from the joint statistics of \X and \Y. This
approach yields an exact set of self consistent equations for the coding rules
X \to \tX and \tX \to \Y. Solutions to these equations can be found by a
convergent re-estimation method that generalizes the Blahut-Arimoto algorithm.
Our variational principle provides a surprisingly rich framework for discussing
a variety of problems in signal processing and learning, as will be described
in detail elsewhere
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