Questions tagged [euclidean]
Euclidean distance is the intuitive notion of a 'straight-line' distance between two points in a Euclidean space.
158 questions
0 votes
0 answers
76 views
Comparing Parametric Curves: Fourier Series-Based Similarity Metric
Developing a Similarity Metric for Parametric Curves Using Fourier Series I'm exploring ways to compare parametric curves on the xy-plane using their Fourier series representations. My goal is to ...
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1 vote
1 answer
185 views
Comparison of two test metrics
I'm trying to compare two test metrics (Metric A and Metric B) to determine which one better predicts a delta value, which represents a Euclidean difference. I am unsure how to determining which ...
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2 votes
1 answer
156 views
Are there strategies for measuring accuracy of Euclidean distance-based similarity without ground truthing?
I have subjects with about 200 features each. These feature vectors are stored in a vector database, where similarity searching with Euclidean distance is used to find subjects that are similar to a ...
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2 votes
0 answers
113 views
Using one distance metric on another distance matrix
In general, is it correct to use one distance metric on another distance matrix? For example, is it valid to use Euclidean metric on Jaccard distance matrix as input data in algorithms? or any other ...
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2 votes
1 answer
419 views
What is the standard threshold value that is best for accuracy when employing Euclidean distance as a metric for gauging textual similarity?
I'm using Euclidean distance as a metric to compare two sentences for similarity while clustering them using my custom incremental KMeans algorithm. The current threshold value I'm using is 0.7 which ...
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1 vote
0 answers
110 views
Panel data clustering - how to assess the distance between individuals when the data are multivariate and longitudinal?
I have an (unbalanced) panel dataset with 20 countries, 57 years, and 8 variables, and I would like to cluster the countries according to their dynamic trend in these variables (whether using kmeans ...
2 votes
1 answer
480 views
Normalizing Euclidean distance by the length of the vectors [closed]
Suppose I have 4 vectors, the first 2 vectors are of length 4 and the last 2 vectors are of length 400. all values in the vectors range from 0.5 to 0.6. The Euclidean distance between the last 2 ...
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2 votes
2 answers
2k views
Correlation vs Euclidean distance as measures of similarity or closeness between data points with an outlier
I am interested in the comparison of Pearson correlation and Euclidean distance as measures of similarity between data points. Suppose I have 4 data points, w, x, y, z, in a multidimensional space, ...
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0 votes
1 answer
606 views
Word embedding and Euclidean distance
Does a transformation exist that allows to use of the Euclidean distance with the word embeddings? The Cosine distance could be a problem in my case. For example, what if I translate the vector to a ...
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1 vote
1 answer
734 views
Best way for measuring dispersion in two dimensional, continuous data
I have a list of coordinates for where different people live over an eight-year period. They are repeat cross-sections of populations served by several county agencies for free workforce training for ...
- 422
2 votes
1 answer
912 views
How to choose the Normalization method for a co-occurence matrix?
I have a co-occurrence matrix about hashtags usage (The value in the cell means the number of times two hashtags appear together in a single tweet), it is transformed from a 2-mode matrix. Now I want ...
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2 votes
0 answers
342 views
normalizing euclidean distance
I asked a question in SO but was told it is more appropriate here. I'm trying to compute the euclidean distance with vectors of different lengths. ...
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1 vote
0 answers
405 views
What are the downsides of using euclidean distance for hierarchical clustering of a correlation matrix?
Apologies if this has been answered elsewhere, but I couldn't find any answers discussing this specific question. I am lacking some notion on clustering using euclidean vs correlation distance, when ...
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164 views
Euclidean distance between points in high dimensions
On Wikipedia there's a statement: When a measure such as a Euclidean distance is defined using many coordinates, there is little difference in the distances between different pairs of samples. Is ...
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171 views
Is cosine similarity enough to measure word embedding similarity?
Is cosine similarity a good metric to measure word embedding similarity? Suppose that we have two vectors of word embedding in same direction but with different length( first one with len=1 and second ...
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