Robust PCA neural networks for random noise reduction of the data

Stanislaw Osowski, Andrzej Majkowski, Andrzej Cichocki

Research output: Contribution to journalConference articlepeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)3397-3400
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume4
Publication statusPublished - 1997
Externally publishedYes
EventProceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 5) - Munich, Ger
Duration: 21 Apr 199724 Apr 1997

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