Similarity between Euclidean and cosine angle distance for nearest neighbor queries

By Gang Qian, Shamik Sural, Yuelong Gu, Sakti K. Pramanik. ACM Symposium on Applied Computing, 2004

352 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/7635e2071dbea24e70bb170e480d24f868bbef7b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Heng Yin
2. Brian Testa
3. Qian Feng
4. Yao Cheng
5. Chengcheng Xu
6. Rundong Zhou

In Signal Processing

1. Heng Yin
2. Qian Feng

Citations in the corpus

• Scalable Graph-based Bug Search for Firmware Images (2016, methodology): “We experiment on two classical distance metrics defined in the hashing space: Euclidean distance and the cosine distance [47] in our bug search problem.”
• DTN routing in a mobility pattern space (2005, result): “This may be explained by the fact that when the number of dimensions of the space is high, there is a strong correlation between those metrics, as shown by Qian et al. [18].”