Summary of Non-negative Tensor Mixture Learning For Discrete Density Estimation, by Kazu Ghalamkari et al.
Non-negative Tensor Mixture Learning for Discrete Density Estimation
by Kazu Ghalamkari, Jesper Løve Hinrich, Morten Mørup
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 The paper presents a unified framework for non-negative tensor decomposition using the expectation-maximization (EM) algorithm, which optimizes the Kullback-Leibler divergence. The framework establishes a connection between low-rank decomposition and many-body approximation, allowing for simultaneous updates of all parameters in the M-step. This approach can handle various low-rank structures, including CP, Tucker, and Train decompositions, as well as their combinations and robust adaptive noise modeling. Experimental results show that the framework outperforms conventional tensor-based approaches in discrete density estimation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to break down complex data into simpler pieces using a type of math called expectation-maximization (EM). This helps us understand and analyze large amounts of data more effectively. The authors show how their method can be used for different types of data decomposition, such as CP, Tucker, and Train, as well as combining these methods to get even better results. They also demonstrate that this approach works well for a specific type of problem called discrete density estimation. |
Keywords
* Artificial intelligence * Density estimation
