Summary of Surpassing Cosine Similarity For Multidimensional Comparisons: Dimension Insensitive Euclidean Metric, by Federico Tessari et al.
Surpassing Cosine Similarity for Multidimensional Comparisons: Dimension Insensitive Euclidean Metric
by Federico Tessari, Kunpeng Yao, Neville Hogan
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 investigates the limitations of traditional metrics used for comparing high-dimensional quantities in artificial intelligence (AI) applications. Specifically, it analyzes the effects of dimensionality on cosine similarity, a widely-used metric in natural language processing and recommender systems. The authors reveal that as dimensions increase, the interpretability of cosine similarity diminishes due to its dependency on vector dimensions, leading to biased outcomes. To address this issue, they introduce a novel metric called Dimension Insensitive Euclidean Metric (DIEM), which demonstrates superior robustness and generalizability across dimensions. DIEM eliminates biases and maintains consistent variability, making it a reliable tool for high-dimensional comparisons. The paper provides an example of the advantages of DIEM over cosine similarity in a large language model application. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how AI compares things that have many features or characteristics. Right now, we use metrics like cosine similarity to compare these things, but as the number of features gets bigger, these metrics don’t work so well anymore. This is because they rely on the size of the “vectors” (think of them like special kinds of lines) used to describe the features. The authors create a new metric called DIEM that ignores how big or small the vectors are and just focuses on how similar or different the things being compared are. This makes it more reliable and accurate, especially when comparing things with many features. |
Keywords
* Artificial intelligence * Cosine similarity * Large language model * Natural language processing
