Spectral decomposition provides a canonical representation of an operator over a vector space in terms of its eigenvalues and eigenfunctions. The canonical form often facilitates discussions which, otherwise, would be complicated and involved. Spectral decomposition is of fundamental importance in many applications. The well-known GLR theory generalizes the classical result of eigendecompositio...

Temporal decomposition (TD) is an e ective technique to compress the spectral information of speech through orthogonalization of the matrix of spectral parameters leading to an e cient rate reduction in speech coding applications. The performance of TD is function of the parameters used. Although \decomposition suitability" of a parameter set is typically de ned on the basis of \phonetic releva...

The main obstacle for obtaining fast domain decomposition solvers for the spectral element discretizations of the 2-nd order elliptic equations was the lack of fast solvers for local internal problems on subdomains of decomposition and their faces. As was recently shown by Korneev/Rytov, such solvers can be derived on the basis of the specific interrelation between the stiffness matrices of the...

The theory of the D Wold decomposition of homogeneous random elds is e ective in im age and video analysis synthesis and model ing However a robust and computationally ef cient decomposition algorithm is needed for use of the theory in practical applications This pa per presents a spectral D Wold decomposition algorithm for homogeneous and near homoge neous random elds The algorithm relies on t...

In this paper, we propose a speech enhancement method based on spectral magnitude estimation. We modify the noise estimation from the minimum statistics method and combine with a maximum a posterior (MAP) decomposition, using the Rice-conditional probability and a non-Gaussian statistic model of the speech. We derive two versions of magnitude decomposition and magnitude-phase decomposition and ...

Spectral clustering has attracted much research interest in recent years since it can yield impressively good clustering results. Traditional spectral clustering algorithms first solve an eigenvalue decomposition problem to get the low-dimensional embedding of the data points, and then apply some heuristic methods such as k-means to get the desired clusters. However, eigenvalue decomposition is...