Robust PCA neural networks for random noise reduction of the data
Stanisław Osowski , Andrzej Majkowski , A Cichocki
AbstractThe paper presents a 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 the 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 examples of 1-dimensional and 2-dimensional data and the results of numerical experiments are included
|Book||, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1997. ICASSP-97, vol. 4, 1997|
|Keywords in English||1D data, 2D data, compression ratio, data compression, filtering theory, image coding, image restoration, loss tolerance, lossy compression, lossy decompression, neural nets, noise filtering, noise strength, principal component analysis, random noise, random noise reduction, robust PCA neural networks|
|Publication indicators||= 0; = 2; = 8.0|
|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.