6533b854fe1ef96bd12af3ac
RESEARCH PRODUCT
Higher-order Nonnegative CANDECOMP/PARAFAC Tensor Decomposition Using Proximal Algorithm
Tapani RistaniemiDeqing WangFengyu Congsubject
ta113ta213signaalinkäsittelyComputationproximal algorithmnonnegative CAN-DECOMP/PARAFACalternating nonnegative least squares010103 numerical & computational mathematics01 natural sciencesLeast squares03 medical and health sciences0302 clinical medicinetensor decompositionblock principal pivotingConvergence (routing)Decomposition (computer science)Tensor decompositionOrder (group theory)0101 mathematicsMulti way analysisAlgorithm030217 neurology & neurosurgeryBlock (data storage)Mathematicsdescription
Tensor decomposition is a powerful tool for analyzing multiway data. Nowadays, with the fast development of multisensor technology, more and more data appear in higherorder (order > 4) and nonnegative form. However, the decomposition of higher-order nonnegative tensor suffers from poor convergence and low speed. In this study, we propose a new nonnegative CANDECOM/PARAFAC (NCP) model using proximal algorithm. The block principal pivoting method in alternating nonnegative least squares (ANLS) framework is employed to minimize the objective function. Our method can guarantee the convergence and accelerate the computation. The results of experiments on both synthetic and real data demonstrate the efficiency and superiority of our method. peerReviewed
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-05-01 |