Detection of Noisy and Corrupted Data Using Clustering Techniques
Kui Cai, Kees A. Schouhamer Immink · 2018
We investigate machine learning based on clustering techniques that are suitable for the detection of n-symbol words of q-ary symbols transmitted over a noisy channel with partially unknown characteristics. We consider the detection of the n-symbol q-ary data as a classification problem, where objects are recognized from a corrupted vector, which is obtained by an unknown corruption process.