Fast tensor signal filtering using fixed point algorithm

Subspace-based methods rely on the selection of leading eigenvectors, associated with dominant eigenvalues. They have been extended to tensor data processing, such as denoising. Usually EVD (eigenvalue decomposition) is performed and data projection on leading eigenvectors results in noise reduction. Tensor processing methods, in particular multiway Wiener filtering algorithm, include an ALS (alternating least squares) loop, which involves several EVDs. Fixed point algorithm is a faster method than EVD to estimate a fixed number of eigenvectors. In this paper, we adapt fixed point algorithm to the estimation of only the required leading eigenvectors in a tensor processing framework. We adapt inverse power method to estimate the required noise variance. We provide a comparative study in terms of speed through an application to hyperspectral image denoising.