The euclidean tag has no wiki summary.
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1answer
33 views
Nonlinear manifold learning and distances between projected points
If I use a manifold learning method to project some data points into a low dimensional space, what will be the distances between the projected points? Can I use Euclidean distance? If the distances ...
1
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1answer
47 views
How to compute a measure of distance between sites with continuous variables?
I have a dataframe with each row being a different site (51 sites), and each column being mean values of a different continuous environmental variable (19 variables).
I am trying to calculate a ...
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0answers
19 views
Euclidean distance prove [duplicate]
I know that Euclidean distance is a measure that may be used to compute the similarity between two vectors. Given a query q and documents d 1, …, dn, we may rank the documents d 1, …, dn in the ...
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0answers
69 views
How to make results using Hellinger distance comparable with Euclidean distance outputs?
I have a two kinds of data for the same geographic region. One is presence-absence data of species (for amphibians, reptiles and birds) and the other has several environmental variables for the same ...
2
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1answer
607 views
K-means Mahalanobis vs Euclidean distance
I currently am trying to cluster "types" of changes on bitemporal multispectral satellite images.
I applied a thing called a mad transform to both images, 5000 x 5000 pixels x 5 bands.
Each band is a ...
3
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1answer
111 views
Are there methods to learn a projection method into euclidean space, given a set of pairwise distances?
Basically, right now I am trying to do some nearest-neighbour searching on an approach where I don't have points in Euclidean space. To do a nearest-neighbour search currently, I take the query and ...
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3answers
700 views
Using a cosine similarity does not work for any dataset
I have a clustering algorithm, where if I use an euclidian distance as similarity, it works well on any dataset. If I replace it by a cosine similarity (see my code bellow), it will give a degenerate ...
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5answers
1k views
Euclidean distance is usually not good for sparse data?
I have seen somewhere that classical distances (like Euclidean distance) become weakly discriminant when we have multidimensional and sparse data. Why? Do you have an example of two sparse data ...
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0answers
40 views
Given 2 lists with the same values but different ordering, how close are they to each other?
I've implemented an application that collects some relevant data about certain topics, so for each topic I get a list of strings. I want to make a test to see if the data makes sense.
I thought of ...
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1answer
769 views
Gower's dissimilarity index
I would like to ask a question about Gower similarity/dissimilarity index.
Is it ok to use the Gower dissimilarity measure with Ward linkage clustering?
I was reading that the Gower similarity index ...
