Spectra-Guided Neural Tucker Factorization
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Abstract (arXiv)
This paper proposes Spectra-Guided Neural Tucker Factorization (SG-NTF) for High-Dimensional and Incomplete (HDI) tensor completion. Circumventing discrete representational limits, SG-NTF maps scalar timestamps into a continuous spectral space to abstract temporal periodicities. Concurrently, a Spatio-Temporal Co-Gating (STCG) mechanism explicitly filters latent interactions via multiplicative modulation on spatiotemporal contexts. Evaluations on real-world HDI tensors verify that SG-NTF maintains competitive completion accuracy with parameter efficiency.
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References · 8
- 2017Attention Is All You Needcited by 60,361
- 2016Optimization Methods for Large-Scale Machine Learningcited by 1,150
- 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
- 2017Neural Collaborative Filteringcited by 1,111
- 2013Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programmingcited by 724
- 2019Bayesian Temporal Factorization for Multidimensional Time Series Predictioncited by 47
- 2024FastSTI: A Fast Conditional Pseudo Numerical Diffusion Model for Spatio-temporal Traffic Data Imputationcited by 4
- 2022An Adaptive Alternating-direction-method-based Nonnegative Latent Factor Modelcited by 3
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