Title :
Analysis of feature concatenation operation on vector spaces
Author :
Özay, Mete ; Vural, Fatos T Yarman
Author_Institution :
Bilgisayar Muhendisligi Bolumu, ODTU, Ankara, Turkey
Abstract :
In this study, the theoretical and experimental analysis of feature space concatenation operation is introduced. This operation is widely used for data fusion and ensemble learning. Following the analysis, a new performance measure which is called Vectorization Measure (VM) is introduced. VM enables the estimation of the separability capacity of the fusion space by analyzing the sample margin distributions on the feature vector subpsaces.
Keywords :
sensor fusion; vectors; data fusion; ensemble learning; feature space concatenation; vector spaces; vectorization measure; Art; Artificial neural networks; Biometrics; Face; Image classification; Kernel; Machine learning;
Conference_Titel :
Signal Processing and Communications Applications Conference (SIU), 2010 IEEE 18th
Conference_Location :
Diyarbakir
Print_ISBN :
978-1-4244-9672-3
DOI :
10.1109/SIU.2010.5651471