6 accepted papers
We developed a new tensor-based technique for connectivity analysis and applied it to the EEG data of 10 professional eSports players and 10 novices (control group) collected during 4 different oddball paradigms. The proposed technique utilizes a low-rank approximation of the Granger Causal autoregr…
Weighted canonical polyadic (CP) tensor decomposition appears in a wide range of applications. A typical situation where the weighted decomposition is needed is when some tensor elements are unknown, and the task is to fill in the missing elements under the assumption that the tensor admits a low-ra…
Decomposition of symmetric tensors has found numerous applications in blind sources separation, blind identification, clustering, and analysis of social interactions. In this paper, we consider fourth order symmetric tensors, and its symmetric tensor decomposition. By imposing unit-length constraint…
Canonical polyadic decomposition (CPD), also known as PARAFAC, is a representation of a given tensor as a sum of rank-one tensors. Traditional method for accomplishing CPD is the alternating least squares (ALS) algorithm. This algorithm is easy to implement with very low computational complexity per…
Canonical polyadic decomposition of tensor is to approximate or express the tensor by sum of rank-1 tensors. When all or almost all components of factor matrices of the tensor are highly collinear, the decomposition becomes difficult. Algorithms, e.g., the alternating algorithms, require plenty of i…
In this paper, we propose a low-rank tensor deconvolution problem which seeks multiway replicative patterns and corresponding activating tensors of rank-1. An alternating least squares (ALS) algorithm has been derived for the model to sequentially update loading components and the patterns. In addit…