Robust PCA neural networks for random noise reduction of the data
Stanisław Osowski , Andrzej Majkowski , Andrzej Cichocki
The paper presents principal component analysis (PCA) approach to the reduction of noise contaminating the data. The PCA performs the role of lossy compression and decompression. The compression/decompression provides the means of coding the data and then recovering it with some losses, dependent on the realized compression ratio. In this process some part of information contained in the data is lost. When the loss tolerance is equal to the noise strength, the noise and the loss tolerance are augmented and the decompressed signal is deprived of noise. This way of noise filtering has been checked on the examples of 1-dimensional and 2-dimensional data and the results of numerical experiments have been included in the paper.
|Journal series||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, ISSN 0736-7791|
|Publication size in sheets||0.5|
|Publication indicators||= 5; = 8.0; = 2|
|Citation count*||9 (2020-09-09)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.