In a corporate setting in the UK, current data analysis focuses on calculating distances between two addresses using GPS coordinates. Remarkably, 99% of these calculations only consider straight-line distances. This approach, while interesting, does not account for real-world travel routes. Google’s API offers a solution by providing travel distances by car and public transport. However, Google’s terms of service limit the storage of query results and the number of API calls. As a result, there is a pressing need for alternative tools and data sources that comply with UK regulations to enhance distance calculation accuracy.
Joachim Schork
@JoachimSchork
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Feb 3 Missing data is a common issue in data analysis, and Little’s MCAR test is a statistical method to determine if the missing values in a data set occur completely at random. Properly understanding the mechanism behind missing data is critical for ensuring accurate analyses and
Information Theory Papers
@Encoding
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Apr 15 The Exploratory Study on the Relationship Between the Failure of Distance Metrics in High-Dimensional Space and Emergent Phenomena.
Lucas Nestler
@_clashluke
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Feb 4 A new paper shows that using L2 distance is better than using a dot product for classification loss.* More expressive
* Higher stability
* Easier to learnL2 distances have been a long-standing problem. They are frequently used in contrastive learning (Barlow Twins) but
CK
@chronokaizen
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Apr 17 When we fail to head towards any + side of the range deviation, we naturally come towards the negative side of the deviation.Plugged some values of RTH Gap, OHLC statmap and Session along with Range Deviations into the new alerts correlation analysis server.
Frank Nielsen
@FrnkNlsn
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Feb 2 Information Geometry for Machine Learning
Distances between multivariate normal/Gaussian distributions:① Approximate Fisher-Rao distance② Pullback distances from the SPD cone to Gaussian manifold
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