100805510.zip - We present a new necessary and sufficient condition for essential uniqueness of
the decomposition of a third-order tensor in rank-(L r , L r , 1) terms. We derive a new deterministic
technique for blind signal separation that relies on this decomposition. The method assumes that
the signals can be modeled as linear combinations of exponentials or, more generally, as exponential
polynomials. The results are illustrated by means of numerical experiments.,2020-08-05 02:26:07,下载0次
s.zip - We consider the multichannel blind deconvolution problem where we observe the output of multiple
channels that are all excited with the same unknown input. From these observations, we wish to estimate
the impulse responses of each of the channels. We show that this problem is well-posed if the channels
follow a bilinear model where the ensemble of channel responses is modeled as lying in a low-dimensional
subspace but with each channel modulated by an independent gain. Under this model,,2020-08-05 02:24:42,下载0次
06661921.zip - Tensor based singular spectrum analysis (SSA) has been in-
troduced as an extension of traditional singular value decom-
position (SVD) based SSA. In the SSA decomposition stage
PARAFAC tensor factorization has been employed. Using
tensor factorization methods enable SSA to perform much
better in nonstationary and underdetermined cases. The re-
sults of applying the proposed method to both synthetic and
real data show that this system outperforms the original SSA,
when used for single channel data decomposition in nonsta-
tionary and underdetermined source separation,2020-08-05 02:20:46,下载0次
34637588.zip - Given an instantaneous mixture of some source signals, the
blind signal separation (BSS) problem consists of the identification of
both the mixing matrix and the original sources. By itself, it is a non-
unique matrix factorization problem, while unique solutions can be ob-
tained by imposing additional assumptions such as statistical indepen-
dence. By mapping the matrix data to a tensor and by using tensor
decompositions afterwards, uniqueness is ensured under certain condi-
tions. Tensor decompositions have been studied thoroughly in literature.
We discuss the matrix to tensor step and present tensorization as an
important concept on itself, illustrated by a number of stochastic and
deterministic tensorization techniques.,2020-08-05 02:19:09,下载0次
07590145.zip - A Tensor-Based Method for Large-Scale Blind Source Separation Using Segmentation
Many real-life signals are compressible, meaning that
they depend on much fewer parameters than their sample size. In
this paper, we use low-rank matrix or tensor representations for
signal compression. We propose a new deterministic method for
blind source separation that exploits the low-rank structure, en-
abling a unique separation of,2020-08-05 02:16:39,下载0次