Information Theoretic learning
Facultad de Informática,
describes our efforts to go beyond the second order moment assumption
still prevalent in optimal signal processing and machine learning. We
show how the second norm of the PDF can be estimated directly from data
avoiding an explicit PDF estimation step. The link between PDF moments,
information theory and Reproducing Kernel Hilbert spaces will be
established. Applications to adaptive systems with entropic cost
functions will be demonstrated. A generalized correlation function
called correntopy will be defined and its applications in signal
processing will be outlined. Correntopy leads to new measures of
similarity, to a new definition of dependence subspaces and to new
tests for causality.
Organiza: Amparo Alonso,
Departamento de Computación.