tailieunhanh - Báo cáo hóa học: " Multiway Filtering Based on Fourth-Order Cumulants Damien Muti"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Multiway Filtering Based on Fourth-Order Cumulants Damien Muti | EURASIP Journal on Applied Signal Processing 2005 7 1147-1158 2005 Hindawi Publishing Corporation Multiway Filtering Based on Fourth-Order Cumulants Damien Muti Groupe GSM Institut Fresnel UMR CNRS 6133 EGIM Université Aix-Marseille III . de Saint Jerome 13397 Marseille Cedex 20 France Email Salah Bourennane Groupe GSM Institut Fresnel UMR CNRS 6133 EGIM Universite Aix-Marseille III . de Saint Jerome 13397 Marseille Cedex 20 France Email Received 31 March 2004 Revised 4 November 2004 Recommended for Publication by Chong-Yung Chi We propose a new multiway filtering based on fourth-order cumulants for the denoising of noisy data tensor with correlated Gaussian noise. The classical multiway filtering is based on the TUCKALS3 algorithm that computes a lower-rank tensor approximation. The presented method relies on the statistics of the analyzed multicomponent signal. We first recall how the well-known lower-rank- K1 . KN tensor approximation processed by TUCKALS3 alternating least square algorithm exploits second-order statistics. Then we propose to introduce the fourth-order statistics in the TUCKALS3-based method. Indeed the use of fourthorder cumulants enables to remove the Gaussian components of an additive noise. In the presented method the estimation of the tt-mode projector on the tt-mode signal subspace is built from the eigenvectors associated with the largest eigenvalues of a fourthorder cumulant slice matrix instead of a covariance matrix. Each projector is applied by means of the tt-mode product operator on the tt-mode of the data tensor. The qualitative results of the improved multiway TUCKALS3-based filterings are shown for the case of noise reduction in a color image and multicomponent seismic data. Keywords and phrases multicomponent signals tensors Tucker3 decomposition HOSVD cumulant slice matrix subspace methods. 1. INTRODUCTION In many fields so diverse as chemometric psychology data analysis

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