Signal Processing Techniques Restructure The Big Data Era
Charilaos Petrou, Michael Paraskevas · 2016
Big data science has been developed into a topic that attracts attention from industry, academia and governments. The main objective in Big Data science is to recognize and extract meaningful information from huge amounts of heterogeneous data and unstructured data (which constitute 95% of big data). Signal Processing (SP) techniques and related statistical learning (SL) tools such as Principal Component Analysis (PCA), R-PCA (Robust PCA), Compressive Sampling (CS), convex optimization (CO), stochastic approximation (SA), kernel based learning (KBL) tasks are used for robustness, compression and dimensionality reduction in Big Data arising challenges. This review paper introduces Big Data related SP techniques and presents applications of this emerging field.