Graduate Exam Abstract
Somayeh Hosseini
M.S. Final
October 28, 2014, 9:30 am - 11:00 am
Scott 105
Sparse representations in multi-kernel dictionaries for in-situ classification of underwater objects
Abstract: The performance of the kernel-based
pattern classification algorithms
depends highly on the
selection of the kernel function and its
parameters. Consequently in the
recent years there has been
a growing interest in machine learning
algorithms that select the kernel
function automatically from
a predefined dictionary of kernels. In
this work we develop a general
mathematical framework for
multi-kernel in-situ classification that
makes use of sparse representation
theory for automatically
selecting the kernel functions and
parameters that best represent a set of
training samples. We
construct a dictionary of different
kernel functions with different
parametrizations. Using a sparse
approximation algorithm, we represent
the ideal score of each training sample
as a sparse linear
combination of the kernel functions in
the dictionary evaluated at all training
samples. Moreover,
we incorporate the high-level
operator’s concepts into the learning
by using the in-situ learning
for the new unseen samples whose
scores can not be represented suitably
using the so far selected
representative samples. Finally, we
evaluate the viability of this method for
in-situ classification
of a database of underwater object
images.
Adviser: Ali Pezeshki
Co-Adviser: Mahmood Azimi
Non-ECE Member: Michael Kirby
Member 3: Edwin Chong
Addional Members: Rockey Luo
Publications:
N/A
Program of Study:
ECE 513
ECE 514
ECE 651
ECE 656
ECE 681
MATH 566
STAT 560
ECE 799
