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Questions tagged [manhattan]

Manhattan Distance is an alternative distance metric to Euclidean Distance. It is calculated by taking the absolute difference between two points.

Formally, it is given by:

$d_1(x, y) = \|x - y\|_1 = \sum\limits_{i=1}^{n} |x_i - y_i|$

As an example:

If $x = (a, b)$ and $y = (c, d)$ then:

Manhattan Distance = $|a - c| + |b - d|$

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Coordinate System's influence on $L$ distances (Manhattan and Euclidean)

I don't understand this picture, which says if we change the coordinate system, we would have the same result for $L_2$ distance, whereas, our result would differ for $L_1$ distance. What does it mean by coordinate system? $(0,0)$ if yes, the…
Fatemeh Asgarinejad
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Very basic question: what is an accepted term for "linear order distance"

In data science we have "Manhattan Distance" as a slang term for Level 1 Distance and "Euclidean Distance" as a slang term for Level 2 Distance. Is there an accepted term for linear distance in memory of cells in different rows in a matrix? That is,…
Jack Parsons
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Estimating time to travel between two lat/longs

I'm trying to create an offline estimator for how long it would take to get from one lat/long to another. Two approaches I have come across are the Haversine distance and the Manhattan distance. What I'm thinking of doing is calculating both of them…
C Murphy
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Cosine vs Manhattan for Text Similarity

I'm storing sentences in Elasticsearch as dense_vector field and used BERT for the embedding so each vector is 768 dim. Elasticsearch gives similarity function options like Euclidean, Manhattan and cosine similarity. I have tried them and both…
Mohy Mohamed
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