be an orthonormal basis of H consisting of eigenvectors of A where {ek : k ∈ N} is a set of eigenvectors of A corresponding to nonzero eigenvalues of A and {fm : m ∈ I} is a set of eigenvectors of A corresponding to the zero eigenvalue (we know that we may assume that I is a countable set by exercise 6.10). We make the assumption that the elements of {ek : k ∈ N} are ordered so that the first e...

An intruder detection method by using time variant Multiple Input Multiple Output (MIMO) channels has been proposed (MIMO sensor). In this letter, the detection performance using eigenvectors, which are obtained by singular value decomposition for MIMO channel matrix, is evaluated in actual indoor propagation environment. Moreover, a new diversity method using multiple eigenvectors is proposed ...

Second order statistics estimates in the form of sample eigenvalues and sample eigenvectors give a sub optimal description of the population density. So far only attempts have been made to reduce the bias in the sample eigenvalues. However, because the sample eigenvectors differ from the population eigenvectors as well, the population eigenvalues are biased estimates of the variances along the ...

Face recognition has advantages over other biometric methods. Principal Component Analysis (PCA) has been widely used for the face recognition algorithm. PCA has limitations such as poor discriminatory power and large computational load. Due to these limitations of the existing PCA based approach, we used a method of applying PCA on wavelet subband of the face image and two methods are proposed...

the analysis of cross-correlations is extensively applied for understanding of interconnections in stock markets. variety of methods are used in order to search stock cross-correlations including the random matrix theory (rmt), the principal component analysis (pca) and the hierachical ‎structures.‎ in ‎this work‎, we analyze cross-crrelations between price fluctuations of 20 ‎company ‎stocks‎‎...

We first show that the eigenvector of a tensor is well-defined. The differences between the eigenvectors of a tensor and its E-eigenvectors are the eigenvectors on the nonsingular projective variety S = {x ∈ P | n

We present a method for calculating some select eigenvalues and corresponding eigenvectors of a given Hamiltonian. We show that it is possible to target the eigenvalues and eigenvectors of interest without diagonalizing the full Hamiltonian, by using any arbitrary physical property of the eigenvectors. This allows us to target, for example, the eigenvectors based on their localization propertie...