Summary of An Adrc-incorporated Stochastic Gradient Descent Algorithm For Latent Factor Analysis, by Jinli Li and Ye Yuan
An ADRC-Incorporated Stochastic Gradient Descent Algorithm for Latent Factor Analysis
by Jinli Li, Ye Yuan
First submitted to arxiv on: 13 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY); 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 This paper presents a novel approach to extracting valuable information from high-dimensional and incomplete (HDI) matrices. The authors propose an ADRC-incorporated SGD (ADS) algorithm, which refines the instance learning error by considering historical and future states, enabling faster and more accurate latent factor analysis. This algorithm is combined with a stochastic gradient descent-based latent factor analysis model to extract valuable information from HDI matrices. Experimental results on two HDI datasets show that the proposed model outperforms state-of-the-art LFA models in terms of computational efficiency and accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how we can take big, complicated data sets with missing pieces and turn them into useful information. The authors came up with a new way to do this called ADRC-incorporated SGD (ADS). It’s like a special kind of GPS that helps the computer learn more quickly and accurately. They tested it on two big datasets and found that it worked better than other ways they tried. |
Keywords
* Artificial intelligence * Stochastic gradient descent
