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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