A Robust Approach to Independent Component Analysis of Signals With High-Level Noise Measurements  

Oleh:

Cao, Jianting ; Murata, N. ; Amari, S. ; Cichocki, A. ; Takeda, T.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 14 no. 3 (May 2003), page 631-645.
Topik: robust; robust; approach; independent component analysis; signals; high - level noise; measurements

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36.7 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

We propose a robust approach for independent component analysis (ICA) of signals where observations are contaminated with high - level additive noise and / or outliers. The source signals may contain mixtures of both sub - Gaussian and super - Gaussian components, and the number of sources is unknown. Our robust approach includes two procedures. In the first procedure, a robust prewhitening technique is used to reduce the power of additive noise, the dimensionality and the correlation among sources. A cross - validation technique is introduced to estimate the number of sources in this first procedure. In the second procedure, a nonlinear function is derived using the parameterized t - distribution density model. This non linear function is robust against the undue influence of outliers fundamentally. Moreover, the stability of the proposed algorithm and the robust property of misestimating the parameters (kurtosis) have been studied. By combining the t - distribution model with a family of light - tailed distributions (sub - Gaussian) model, we can separate the mixture of sub - Gaussian and super -Gaussian source components. Through the analysis of artificially synthesized data and real - world magneto encephalo graphic (MEG) data, we illustrate the efficacy of this robust approach.

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