A modification of nonlinear state-space projections (NSSP) method

Tomasz Przybyła

doi:10.12915/pe.2014.05.31

A modification of nonlinear state-space projections (NSSP) method

Tomasz Przybyła

Department of Cybernetics, Nanotechnology and Data Processing (RAU/RAU10)

Research output: Contribution to journal › Article › peer-review

Abstract

The paper presents a modification of nonlinear state-space projections (NSSP) method. The proposed approach deals with the sub-space estimation problem. In the original NSSP method, the principal component analysis (PCA) is used for the sub-space determination. The classical PCA uses L2 norm which is sensitive to outliers. Thus, in this paper the L1 norm PCA is proposed for a sub-space determination as well as for the final value of the processed signal sample.

Translated title of the contribution	Wykorzystanie składowych głównych z norma{ogonek}L1 do filtracji projekcyjnej
Original language	English
Pages (from-to)	134-137
Number of pages	4
Journal	Przeglad Elektrotechniczny
Volume	90
Issue number	5
DOIs	https://doi.org/10.12915/pe.2014.05.31
Publication status	Published - 2014

Keywords

Embedded space
Nonlinear noise reduction
Projective filtering

ASJC Scopus subject areas

Electrical and Electronic Engineering

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10.12915/pe.2014.05.31

Cite this

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abstract = "The paper presents a modification of nonlinear state-space projections (NSSP) method. The proposed approach deals with the sub-space estimation problem. In the original NSSP method, the principal component analysis (PCA) is used for the sub-space determination. The classical PCA uses L2 norm which is sensitive to outliers. Thus, in this paper the L1 norm PCA is proposed for a sub-space determination as well as for the final value of the processed signal sample.",

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AB - The paper presents a modification of nonlinear state-space projections (NSSP) method. The proposed approach deals with the sub-space estimation problem. In the original NSSP method, the principal component analysis (PCA) is used for the sub-space determination. The classical PCA uses L2 norm which is sensitive to outliers. Thus, in this paper the L1 norm PCA is proposed for a sub-space determination as well as for the final value of the processed signal sample.

KW - Embedded space

KW - Nonlinear noise reduction

KW - Projective filtering

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DO - 10.12915/pe.2014.05.31

M3 - Article

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ER -

A modification of nonlinear state-space projections (NSSP) method

Abstract

Keywords

ASJC Scopus subject areas

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