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BLIND SOURCE SEPARATION OVER SPACE: AN EIGENANALYSIS APPROACH
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DOI：10.5705/ss.202023.0157.png)
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Abstract
En 中文
We propose a new estimation method for the blind source separation model of Bachoc et al. (2020). The new estimation is based on an eigenanalysis of a positive definite matrix defined in terms of multiple normalized spatial local covariance matrices, and therefore can handle moderately high-dimensional random fields. The consistency of the estimated mixing matrix is established with explicit error rates even when the eigen-gap decays to zero slowly. The proposed method is illustrated via both simulation and a real data example.
Keywords:
Eigen-analysis
eigen-gap
high-dimensional random field
mixing matrix
normalized spatial local covariance matrix
Journal
S
IF:
1.2
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67
Citations:
3.8K
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