Summary of Modeling Human Responses by Ordinal Archetypal Analysis, By Anna Emilie J. Wedenborg et al.
Modeling Human Responses by Ordinal Archetypal Analysis
by Anna Emilie J. Wedenborg, Michael Alexander Harborg, Andreas Bigom, Oliver Elmgreen, Marcus Presutti, Andreas Råskov, Fumiko Kano Glückstad, Mikkel Schmidt, Morten Mørup
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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 This paper proposes Ordinal Archetypal Analysis (OAA), a novel framework for analyzing questionnaire-based data with ordinal scales, unlike existing methods that transform ordinal data into continuous scales. The authors introduce Response Bias Ordinal Archetypal Analysis (RBOAA) to learn individualized scales for each subject during optimization. The effectiveness of OAA and RBOAA is demonstrated on synthetic data and the European Social Survey dataset, highlighting their potential to provide deeper insights into human behavior and perception. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to understand people’s answers to questions using special math called Archetypal Analysis (AA). They want to analyze questionnaire data that has ordinal scales, like “strongly agree” or “somewhat disagree”. The old way of doing this involved transforming the data into continuous scales, but this method skips those extra steps. They also introduce a new approach called Response Bias Ordinal Archetypal Analysis (RBOAA) that learns individualized scales for each person during optimization. This paper shows how well these methods work on made-up data and real data from the European Social Survey, which could help us understand people’s behavior and perceptions better. |
Keywords
» Artificial intelligence » Optimization » Synthetic data
