DeepAI AI Chat
Log In Sign Up

GOCPT: Generalized Online Canonical Polyadic Tensor Factorization and Completion

by   Chaoqi Yang, et al.
University of Illinois at Urbana-Champaign

Low-rank tensor factorization or completion is well-studied and applied in various online settings, such as online tensor factorization (where the temporal mode grows) and online tensor completion (where incomplete slices arrive gradually). However, in many real-world settings, tensors may have more complex evolving patterns: (i) one or more modes can grow; (ii) missing entries may be filled; (iii) existing tensor elements can change. Existing methods cannot support such complex scenarios. To fill the gap, this paper proposes a Generalized Online Canonical Polyadic (CP) Tensor factorization and completion framework (named GOCPT) for this general setting, where we maintain the CP structure of such dynamic tensors during the evolution. We show that existing online tensor factorization and completion setups can be unified under the GOCPT framework. Furthermore, we propose a variant, named GOCPTE, to deal with cases where historical tensor elements are unavailable (e.g., privacy protection), which achieves similar fitness as GOCPT but with much less computational cost. Experimental results demonstrate that our GOCPT can improve fitness by up to 2:8 claim dataset over baselines. Our variant GOCPTE shows up to 1:2 fitness improvement on two datasets with about 20 model.


page 1

page 2

page 3

page 4


Inductive Framework for Multi-Aspect Streaming Tensor Completion with Side Information

Low-rank tensor completion is a well-studied problem and has application...

MTC: Multiresolution Tensor Completion from Partial and Coarse Observations

Existing tensor completion formulation mostly relies on partial observat...

Multi-version Tensor Completion for Time-delayed Spatio-temporal Data

Real-world spatio-temporal data is often incomplete or inaccurate due to...

Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination

CANDECOMP/PARAFAC (CP) tensor factorization of incomplete data is a powe...

Applying Differential Privacy to Tensor Completion

Tensor completion aims at filling the missing or unobserved entries base...

Influence-guided Data Augmentation for Neural Tensor Completion

How can we predict missing values in multi-dimensional data (or tensors)...

Beyond the Signs: Nonparametric Tensor Completion via Sign Series

We consider the problem of tensor estimation from noisy observations wit...