Summary of Generalized Least Squares Kernelized Tensor Factorization, by Mengying Lei and Lijun Sun
Generalized Least Squares Kernelized Tensor Factorization
by Mengying Lei, Lijun Sun
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new framework called Generalized Least Squares Kernelized Tensor Factorization (GLSKF) to complete multidimensional tensor-structured data with missing entries. The framework integrates smoothness-constrained low-rank factorization with a locally correlated residual process, enabling the characterization of both global dependencies and local variations. GLSKF uses structured covariance/kernel functions to model local processes and an alternating least squares procedure for model estimation. The framework is evaluated on four real-world datasets across diverse tasks, demonstrating superior performance and scalability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GLSKF is a new way to fix broken data with missing parts. It’s like putting together a puzzle where some pieces are missing. The method uses two main parts: one that looks at the big picture (global dependencies) and another that focuses on the small details (local variations). This combination helps the method do better than other ways of fixing incomplete data. The researchers tested GLSKF on four real-world datasets and found it worked well and was fast. |
