SIMILARITY KERNELS FOR DISTANCE-BASED OUTLIER DETECTION
Ruben Ramirez Padron, PhD candidate
Department of Electrical Engineering and Computer Science
University of Central Florida
FEBRUARY 25, 2011
10:30AM – 11:30PM
Key West Ballroom 218D
Abstract Outlier detection is an important research topic that focuses on detecting abnormal
information in data sets and processes. This talk will address the problem of determining which
class of kernels should be used in a geometric framework for distance-based outlier detection. A
new class of similarity kernels is proposed to tackle this problem. Additionally, a new similarity
score is introduced: the summation kernel similarity score (SKSS)*.* Preliminary experimental
results showed that SKSS compare favorably to the commonly used k-NN kernel similarity score
(kNNSS).
BIO: Ruben Ramirez Padron graduated with a Master degree in Computer Engineering from
UCF in 2009. He graduated with a Bachelor degree in Computer Science from "Universidad
Central de Las Villas", Cuba, in 1996. Currently, he is a PhD candidate at the Department of
Electrical Engineering and Computer Science at UCF, under the guidance of Dr. Avelino
Gonzalez. Ruben's main research interests are: novelty detection, kernel methods for pattern
analysis, and online machine learning.