Abstract
In this research, an improved local projection noise reduction approach with three-mode model of neighborhood is proposed. Firstly, one dimensional time series are embedded into a high dimensional phase space. Secondly, the neighborhood tensor of each reference no overlapping window with several consecutive vectors of reconstructed phase-space is computed rather than neighborhood matrix of each separate vector. Lastly, with the suggested model a higher order singular value decomposition (HOSVD) is performed on the neighborhood tensors to split the three mode data into two orthogonal subspaces: the signal and noise subspaces. Throughout the experiment, the effectiveness of the proposed method is validated with a noisy simulated data — the x component of Rossler system and real biomedical signal contaminated with additive white Gaussian noise. Ill. 3, bibl. 14 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.109.3.169
Highlights
The task of noise reduction is a central theme in a wide variety of fields
The objective of this paper is to investigate the denoising performance of an improved local projection noise reduction approach based on 2D model of neighbors
In order to perform a decomposition of the neighborhood tensors and split the three mode data into two orthogonal subspaces – the signal and noise subspaces – the higher order singular value decomposition (HOSVD) [11, 12] is used
Summary
The task of noise reduction is a central theme in a wide variety of fields. As the biomedical signals and the random noise often have overlapping bandwidths, the conventional methods based on the spectrum analysis did not work well for this data. Several phase space projection methods, based on subspace decomposition, were proposed for application to the problem of additive noise reduction in the context of phase space analysis – the global projections method [2], [5] and the local (nearest neighborhoods) phase spaces method [1,2,3,4], [6].The local projection approach project the data in the neighborhood onto the hyper-plane and bring the flow pattern deviated back to the real dynamics system [6] This approach has achieved nice noise reduction effects and has been applied to the speech signals, biomedical signals, mechanical vibration signals, etc. Tensor approach is compared with its matrixvalued counterpart, which requires stacking the 2D neighbors into one highly structured neighborhood matrix
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