The paper introduces a face recognition method using probabilistic subspaces analysis on multi-module singular value features of face images. Singular value vector of a face image is valid feature for identification. But the recognition rate is low when only one module singular value vector is used for face recognition. To improve the recognition rate, many sub-images are obtained when the face...

In this paper, we study the matrix denoising model $Y=S+X$, where $S$ is a low rank deterministic signal and $X$ random noise matrix, both are $M\times n$. scenario that $M$ $n$ comparably large signals supercritical, fluctuation of outlier singular vectors $Y$, under fully general assumptions on structure distribution $X$. More specifically, derive limiting angles between principal $Y$ their c...

H altitude or impulse response matrix I identity matrix j dummy index M Mach number R correlation coefficient S matrix of singular values STBL left stabiliser position STBR right stabiliser position t, Δt time, time step T projected state matrix multiplied by the instrument TEFL left trailing-edge flap deflection TEFR right trailing-edge flap deflection u system input vector U matrix of past or...

two symmetric matrices, where one matrix has a restricted rank. A comparison of the two cases reveals a remarkable similarity between Eckhart-Young theorem and Ky Fan’s maximum principle. The extremum principles of Ky Fan consider sums and products of eigenvalues of Hermitian matrices. The third part of the talk derives extended rules for rectangular matrices. In this case singular values take ...