Summary of Efficient Nonparametric Tensor Decomposition For Binary and Count Data, by Zerui Tao et al.
Efficient Nonparametric Tensor Decomposition for Binary and Count Data
by Zerui Tao, Toshihisa Tanaka, Qibin Zhao
First submitted to arxiv on: 15 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose ENTED, a novel tensor decomposition method specifically designed for high-dimensional and sparse binary or count data. Traditional tensor decompositions often rely on Gaussian distribution assumptions, which are not suitable for discrete data. The authors address these limitations by introducing a nonparametric Gaussian process (GP) to replace traditional multi-linear structures and incorporating sparse orthogonal variational inference of inducing points to enhance the model’s computational efficiency. The proposed method, ENTED, is evaluated on several real-world tensor completion tasks, demonstrating both better performance and computational advantages compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to analyze big datasets that have lots of zeros in them. It’s called ENTED, which is short for Efficient Nonparametric Tensor Decomposition. Usually, computers use special math tools to understand these kinds of datasets, but they can get stuck when the data doesn’t fit into those rules. The researchers came up with a new way to do it that works better and uses less computer power. They tested it on some real-world problems and showed that it does a better job than other methods. |
Keywords
* Artificial intelligence * Inference
