arXiv:2606.00584v1 Announce Type: cross Abstract: 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
This research is emerging as AI and machine learning models increasingly rely on complex, high-dimensional data, pushing the boundaries of current analytical techniques.
Advanced tensor factorization techniques can significantly improve the efficiency and accuracy of processing large AI datasets, impacting the development and deployment of sophisticated AI models.
The ability to handle High-Dimensional and Incomplete (HDI) tensors more effectively means AI systems can robustly learn from sparser or less perfect real-world data.
- · AI researchers and developers
- · Data-intensive industries
- · Machine learning infrastructure providers
- · Legacy data processing methods
Improved performance and reliability of AI models in applications dealing with complex and incomplete data.
Accelerated development of AI agents and systems that can operate in dynamic, real-world environments with imperfect information.
Potential for new AI applications in fields previously constrained by data quality or dimensionality challenges, such as advanced predictive analytics or complex system control.
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Read at arXiv cs.LG