Accurate Online Tensor Factorization for Temporal Tensor Streams with Missing Values
Dawon Ahn, Seyun Kim, U Kang
Abstract
Given a time-evolving tensor stream with missing values, how can we accurately discover latent factors in an online manner to predict missing values? Online tensor factorization is a crucial task with many important applications including the analysis of climate, network traffic, and epidemic disease. However, existing online methods have disregarded temporal locality and thus have limited accuracy.
Topics & Concepts
Tensor (intrinsic definition)Computer scienceLocalityMatrix decompositionMissing dataData miningTask (project management)FactorizationArtificial intelligenceMachine learningMathematicsAlgorithmEigenvalues and eigenvectorsQuantum mechanicsLinguisticsPhilosophyEconomicsPure mathematicsManagementPhysicsTensor decomposition and applicationsAdvanced Neuroimaging Techniques and Applications