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A Fast Method to Sparse Reconstruction for Near-infrared Spectroscopy

Chao Wu  Tianhong Pan  
【摘要】:Sparse reconstruction is widely popular in many fields.Sparse Bayesian learning(SBL) approach has been applied to capture and recover the spectra variables via a small number of measurements.However,the computational cost of the reconstruction is high because an inverse of large matrix needs to be calculated in each iteration.In this paper,a fast method to sparse reconstruction for near-infrared(NIR) spectra is proposed based on block sparse Bayesian learning framework.Then,the minimum cost function obtained the optimal hyper-parameters is taken by using the fast marginalized likelihood maximization method,which is greatly helpful to reduce the computational burden.Finally,a real NIR spectra is used to verified the proposed method by comparing with full-spectral based PLS and SBL+PLS methods.The experimental results explained that the proposed method has better performance than other two methods.

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