Robust PCA neural networks for random noise reduction of the data

Stanisław Osowski , Andrzej Majkowski , Andrzej Cichocki

Abstract

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.

Author Stanisław Osowski (FoEE / ITEEMIS)
Stanisław Osowski,,
- The Institute of the Theory of Electrical Engineering, Measurement and Information Systems
, Andrzej Majkowski (FoEE / ITEEMIS)
Andrzej Majkowski,,
- The Institute of the Theory of Electrical Engineering, Measurement and Information Systems
, Andrzej Cichocki (WUT)
Andrzej Cichocki,,
- Warsaw University of Technology
Journal seriesICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, ISSN 0736-7791
Issue year1997
Vol4
Pages3397-3400
Publication size in sheets0.5
Languageen angielski
Score (nominal)0
Score sourcejournalList
Publication indicators Scopus Citations = 5; GS Citations = 8.0; WoS Citations = 2
Citation count*9 (2020-09-09)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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