Some data analysis methods yield poor or only adequate information on their own but with data fusion, multiple datasets can be merged to possibly yield more information than when used alone. Data fusion can even be used to merge reduced representations of different parts of the same dataset. Data fusion yields improved results in situations where each set of data to be merged contains informati...

Resumo—Neste artigo, considera-se o uplink de um sistema de comunicação DS-CDMA multiusuário cooperativo, em que cada usuário se comunica com a estação-base com a ajuda de R relays. Uma configuração DS-CDMA cooperativa utilizando espalhamento espectral nos relays é proposta neste trabalho, e para este cenário cooperativo, é formulado um novo modelo trilinear para o sinal recebido que combina os...

Let X be a real-valued three-way array. The Candecomp/Parafac (CP) decomposition is written as X = Y(1) + · · · + Y(R) + E, where Y(r) are rank-1 arrays and E is a rest term. Each rank-1 array is defined by the outer product of three vectors a(r),b(r) and c(r), i.e. y ijk = a i b (r) j c (r) k . These vectors make up the R columns of the component matrices A, B and C. If 2R + 2 is less than or ...

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Product between mode-n unfolding Y(n) of an N-D tensor Y and Khatri-Rao products of (N − 1) factor matrices A(m), m = 1, . . . , n − 1, n + 1, . . . , N exists in algorithms for CANDECOMP/PARAFAC (CP). If Y is an error tensor of a tensor approximation, this product is the gradient of a cost function with respect to factors, and has the largest workload in most CP algorithms. In this paper, a fa...

CANDECOMP/PARAFAC (CPD) approximates multiway data by sum of rank-1 tensors. Our recent study has presented a method to rank-1 tensor deflation, i.e. sequential extraction of the rank-1 components. In this paper, we extend the method to block deflation problem. When at least two factor matrices have full column rank, one can extract two rank-1 tensors simultaneously, and rank of the data tensor...

Joint diagonalization problems of Hermitian or non-Hermitian matrices occur as the final parameter estimation step in several blind source separation problems such as ACMA, JADE, PARAFAC, and SOBI. Previous approaches have been Jacobi iteration schemes and alternating projections. Here we show how the joint diagonalization problem can be formulated as a (weighted) subspace fitting problem so th...

In this communication, we propose a new method to blindly identify the mixing matrix of a possibly underdetermined mixture of sources when input signals are cyclostationary with unknown cyclic frequencies. It relies upon a particular linear operator applied to the observations correlation matrix. Then, taking advantage of the properties of the above transformed matrix, a set of cyclic frequenci...

Tensors are multi-way arrays, and the CANDECOMP/PARAFAC (CP) tensor factorization has found application in many different domains. The CP model is typically fit using a least squares objective function, which is a maximum likelihood estimate under the assumption of i.i.d. Gaussian noise. We demonstrate that this loss function can actually be highly sensitive to non-Gaussian noise. Therefore, we...