Questions tagged [euclidean]
Euclidean distance is the intuitive notion of a 'straight-line' distance between two points in a Euclidean space.
160
questions
0
votes
0
answers
16
views
Can't understand this expression used for quantifying error during gradient checking
I was going through Andrew Ng's course 2 in 'Deep Learning specialization' wherein he talks about gradient checking using two sided distance for approximation.
My question is more about the choice of ...
1
vote
1
answer
41
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 ...
- 41
2
votes
1
answer
32
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 ...
- 23
2
votes
0
answers
56
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 ...
- 71
2
votes
1
answer
68
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 ...
- 21
0
votes
0
answers
18
views
Distance metric for dummy and continous variables
I'm trying to apply the KNN regression model to the data I have at my disposal which contains one dummy variable and two continuous variables (which I have normalized). I was wondering if it is okay ...
- 1
0
votes
0
answers
28
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
300
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 ...
- 41
2
votes
2
answers
624
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, ...
- 21
0
votes
1
answer
347
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 ...
- 171
0
votes
1
answer
224
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 ...
- 362
2
votes
1
answer
536
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 ...
- 33
2
votes
0
answers
188
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.
...
- 143
1
vote
0
answers
299
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 ...
- 11
0
votes
0
answers
105
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 ...
- 1,189