Research Information System
Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, vol.12, no.2, pp.349-359, 2023 (Peer-Reviewed Journal)
Extracting and learning meaningful information from big
data streams paves the way for improving the quality of life of societies and
the development of new technologies in the field of science and engineering. On
the other hand, recent advances in sensor technology, increased availability of
computing power and computer memory reveal that data is not just real-valued,
but large-scale complex-valued datasets must also be dealt with. For this
purpose, for the first time in this study, the performances of the recently
proposed online censoring (OC) based complex-valued recursive least squares
(OC-CRLS) and OC-based augmented CRLS (OC-ACRLS) algorithms are tested on
large-scale regression problems and compared with those of their classical
versions in the literature in detail. Simulation studies show that the OC-CRLS
and OC-ACRLS algorithms significantly shorten the training time in large-scale
regression problems defined in the complex domain without affecting testing
performance in a negative way, due to the advantages of their OC mechanism.
This proves that OC-CRLS and OC-ACRLS algorithms are effective and powerful
algorithms in big data streaming applications that can be defined in the
complex domain.