A Fast Randomized Adaptive CP Decomposition For Streaming Tensors
Le Trung Thanh, Karim Abed‐Meraim, Nguyen Linh Trung, Adel Hafiane
Abstract
In this paper, we introduce a fast adaptive algorithm for CAN- DECOMP/PARAFAC decomposition of streaming three-way tensors using randomized sketching techniques. By leveraging randomized least-squares regression and approximating matrix multiplication, we propose an efficient first-order estimator to minimize an exponentially weighted recursive least- squares cost function. Our algorithm is fast, requiring a low computational complexity and memory storage. Experiments indicate that the proposed algorithm is capable of adaptive tensor decomposition with a competitive performance evaluation on both synthetic and real data.
Topics & Concepts
DecompositionComputer scienceMatrix decompositionTensor (intrinsic definition)Computational complexity theoryMultiplication (music)EstimatorAlgorithmTensor decompositionFunction (biology)Matrix multiplicationMatrix (chemical analysis)Mathematical optimizationMathematicsCombinatoricsStatisticsPhysicsComposite materialEigenvalues and eigenvectorsBiologyQuantumMaterials scienceEvolutionary biologyQuantum mechanicsPure mathematicsEcologyTensor decomposition and applicationsAdvanced Neuroimaging Techniques and ApplicationsSparse and Compressive Sensing Techniques