Questions tagged [euclidean]
Euclidean distance is the intuitive notion of a 'straight-line' distance between two points in a Euclidean space.
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questions
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40 views
Manhattan vs Euclidian Distance Measure [duplicate]
In which case we should pickup Manhattan distance and when we should use euclidian distance measure.
To my understanding both are used for continues numeric data(not like cosine or others who works ...
0
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1answer
25 views
How to find the list of nearest vectors if ony a vector is given?
I know there are many ways to compute similarity of two different non-zero vectors but is it possible to get a list of nearest vectors whose values are continous given a single continous vector.
Lets ...
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2answers
124 views
How Rapidminer handle same distance for KNN Algorithm
Actually I already asked in rapidminer forum, but no one has given an answer yet..
https://community.rapidminer.com/discussion/55963/how-k-nn-algorithms-work-with-same-distance-in-rapidminer#latest
...
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0answers
28 views
Calculate Distance between two vectors and estimate goodness of fit to preestablished histogram shapes
I have a squared gene co-expression correlation matrix of many thousands of pairwise correlations among variables (10290^2 aprox). Each row/column represents a different gene and its pairwise ...
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0answers
33 views
the accuracy of covariance between two high-dimensional vectors
Question
Is the covariance between high-dimensional vectors less accruate than covariance between two vectors in low-dimensional vecotrs?
I am asking this questio to check if there is a need for '...
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0answers
7 views
Is there any data distribution that can be separated by an euclidean classifier but not by a linear discriminant analysis?
So leaving out the mathematical part, i am just interested in a data distribution which can be separated by an euclidean classifer(which takes the center of mass of - and + class and there is a ...
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0answers
61 views
Difference between standardizing variables and using Mahalanobis distance
I am wondering how and/or why the Mahalanobis distance is different from using the Euclidean distance on standardized variables?
9
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1answer
912 views
Why is Kullback-Leilbler divergence a better metric for measuring distance between two probability distributions than squared error? [duplicate]
I know that KL-divergence is a metric that is more suitable when we want to measure the distance between numbers which a probability form. However, I am still confused what is the benefit of using KL-...
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2answers
53 views
Clustering high dimensional data
I was going through this wiki page on clustering in high dimensions and I don't understand the following statement there. Can someone explain to me what this means?
The concept of distance becomes ...
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1answer
205 views
Difference between Euclidean ,Pearson, Geodesic and Mahalanobis distance metrics
Given a set of samples $X$. We are tasked to find an appropriate distance metric for $X$ from the given options which are
Euclidean
Pearson
Geodesic and
Mahalanobis distance metrics.
To solve this, ...
0
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0answers
15 views
Feature analyzing methods
I am a little confiused how to interpret following situation:
I am trying to implement a image classification task using hog+SVM.
For that i tried to analyze and understand the properties of the ...
1
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1answer
34 views
What is the specific normalization chi2 in seqdist?
In the documentation for the seqdist() function it is noted that there is "...a specific normalization for"CHI2" and "EUCLID". See the Details section." (p.60). But in the details section there is no ...
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13 views
Test difference of means between subgroups
I have a collection of N samples from which I have computed all pairwise Euclidean distances.
The samples can be divided in 3 distinct subgroups.
I want to test if the average distance of groups is ...
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0answers
31 views
Is my variable considered okay to use in k-means clustering with Euclidean distance?
I was wondering if I can use regular kmeans() in R with my variable "number of drug prescriptions" which equals a number between 1-25. From what I've read k-means ...
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0answers
58 views
Set similarity as weight to ratings
I have a problem deciding which similarity function to use.
I want to find the similarity between the users based on their requirements about computer performance metrics normalized to 1. Each user ...
3
votes
1answer
101 views
What to do when results of hiearchical, k-means elbow, and k-means silhoutte disagree?
I am conducting a cluster analysis involving 60 subjects and 5 continuous variables.
After appropriate scaling, I performed hierarchical clustering with Euclidean distance and complete linkage, and ...
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0answers
162 views
How to improve Pairwise Euclidean Distance for Similarity Measure
I am trying to identify the most similar stations between two DataFrames like below:
...
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0answers
356 views
Dot Product and Distance Matrix
If we want to calculate the squared distance between 2 vectors, $x$ and $y$, we use the dot product:
$$||x-y||^2 = (x-y)(x-y)^T = xx^T - 2xy + yy^T$$
The question is, how to generalize this concept ...
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1answer
478 views
Is one-hot encoding and standardization of data equivalent to Gower's distance?
For clustering and other techniques for mixed data (numerical and categorical), Gower's distance is usually more preferred than Euclidean distance because the former computes distance differently for ...
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0answers
192 views
Clustering: choosing correlation-based over euclidean distance (fviz_dist)
I'm trying to cluster some business data. You can imagine the data as describing customers (age, no. of purchases, satisfaction after the first purchase, etc.). I work with normalised data. I also ...
2
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1answer
97 views
$L_2$ norm of product of two vectors
Let's assume we have two matrices $A^{d\times 1}$ and $B^{1 \times e}$, and we define their product as $C^{d\times e}$. Assuming $A,B$ are real valued with all entries in $[-1,1]$.
I can intuitively ...
2
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1answer
63 views
Distance metric with characteristics of cosine and Manhattan
I'm working on a project where I want to find similarities between groups of events. So far I have expressed groups of events as vectors of event counts and computing similarities between them. I'm ...
0
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1answer
52 views
Euclidean distances in spaces of different dimensions
A modest attempt to illustrate my question:
Setup
Consider a survey where you have to choose if you like to do an activity or not. You do this for a number of activities $A_1,...,A_8$. In addition, ...
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1answer
394 views
How k-means computes cluster centroids differently for each distance metric?
K-means computes cluster centroids differently for each distance metric. I don't know why the way of computing the centroid is dependent of the distance measure.
I don't know how we compute the ...
0
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1answer
54 views
Similarity between 2 profiles (observations). Is it possible to generate a % similarity?
I have multiple profiles for 10 different people. Each person has been measured for 5 different continuous variables of different magnitudes. So my dataframe is 10x5 where each row represents a person ...
1
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1answer
201 views
Does it make sense to cluster asymmetric binary data with Ward's method?
I'm working with 104x42 data set where all variables are (asymmetric) binary (0-1). I've read that Ward's linkage method doesn't work theoretically properly with binary data beaucause it requests ...
9
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1answer
674 views
My neural network can't even learn Euclidean distance
So I'm trying to teach myself neural networks (for regression applications, not classifying pictures of cats).
My first experiments were training a network to implement an FIR filter and a Discrete ...
1
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1answer
90 views
What is the difference between these distances in self organized maps
I am building an anomaly model and am confused between these distances below.
What is the difference between these distances in self organized maps.
som.iris$distances
dist(som.iris$codes[[1]])
As ...
1
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1answer
30 views
Hierarchical Cluster Analysis of 100 objects with 114 variables each
I'm intending to make a cluster analysis of 100 objects. I've read a couple of books and determined that a Hierarchical agglomerative procedure with Ward's linkage method should be used in my case. As ...
2
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0answers
332 views
Obtain within-group Gram matrix out of distance matrix
Gram matrix
Let $\bf X$ be a n x p dataset with columns (variables) centered. Then p x p $\bf X'X$ is the total scatter matrix ...
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0answers
183 views
Common methods to calculate total distance from data with categorical, continuous and counting variables
I have a data set with categorical, continuous and counting variables. I want to be able to use a method that will give me a distance for each pairwise data point.
From my understanding, each type ...
4
votes
2answers
605 views
Is the maximum bound of Euclidean distance between two probability distributions equal to $\sqrt{2}$?
I used Euclidean distance to compute the distance between two probability distribution. The example of computation shown in the Figure below.
As my understanding, the maximum distance occur while $...
2
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0answers
82 views
Recovering a distance matrix from nonnegative sparse correlation matrix?
After doing extensive literature research in all sorts of science I am completely puzzled. I am trying to find out what the state-of-the-art techniques would be to recover a (let's say euclidean) ...
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0answers
27 views
Shrinking the mean: distance loss function and risk function
I'm reading some slides about the shrinkage of the mean and I cannot understand some results.
Assume an n-dimensional vector $\mathbf{x} \sim N(\mu, I_n)$. We are interested in obtaining an estimate ...
3
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0answers
136 views
Which dimensionality reduction technique preserves the k nearest neighbors (euclidean space)?
I'm looking for a lower dimensional projection of data such that the k nearest neighbors (in Euclidean space) in high dimensions remain the k nearest neighbors in low dimensions. I found that Isomap ...
0
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1answer
136 views
Normalized weighted Euclidian Distance
Hi Guys I would like to get an euclidian distance and be able to compare multiple set o data but I am not sure how to normalized it properly
Each data set have values ranging from (-100->100) and ...
1
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0answers
541 views
Distance Matrix for Big Data
I have been struggling to create a distance matrix for some Big Data (800,000x20). I have tried R (dist function), Matlab (pdist function), and cloud computing (to increase RAM).
Ultimately, the ...
0
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1answer
719 views
Distance between all the dots in a scatterplot in R [closed]
Is there any possibility in R to get a list of distances between all the points in a Scatterplot? For instance, this Scatterplot:
So, I would like to obtain a list of distances among all the points.
4
votes
1answer
3k views
Explanation for MSE formula for vector comparison with “Euclidean distance”?
I study the loss functions for regression tasks with artificial neural networks. In case of evaluating loss with Mean Squared Error for multidimensional outputs I read the following usual formula ...
0
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1answer
199 views
Analysis or Comparison of Euclidean Distance matrix
Related: Average distance in distance matrix
I'm looking for some way to compare euclidean distance matrices. The matrices I need to compare will have constant number of rows but varying number of ...
0
votes
1answer
168 views
Mathematical term for finding the variance of the euclidean distance between two matrices of the same dimension?
Is there any mathematical term for carrying out this procedure. I am finding the euclidean distance between the rows of the matrices by treating them as individual vector. Then I get a single vector A ...
0
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0answers
371 views
Why is only Euclidean distance allowed to be used for Ward's method? [duplicate]
Using scipy, I noticed that I am allowed to use only Euclidean distance for Ward's method.
Is it because Ward's uses Error Sum of Squared?
What if I use Ward's method with cosine similarity?
Cosine ...
1
vote
1answer
37 views
Measuring simmilarity of observations (non numeric)
I have a dataset of format :
day,measurement1,measurement2
1,a,b
1,a,c
1,f,s
2,a,b
2,a,c
2,f,g
3,a,d
3,a,q
3,f,s
In this example day1 is more similar with day2 ...
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0answers
335 views
Dummy data clustering: OK to apply Euclidian distance hierarchical clustering on Jaccard similarity matrix?
I created dummy variables (binary data) from categorical variables where I want to partition N subjects into multiple classes by some clustering method. I created a Jaccard similarity index matrix for ...
10
votes
1answer
15k views
Cosine Distance as Similarity Measure in KMeans [duplicate]
I am currently solving a problem where I have to use Cosine distance as the similarity measure for k-means clustering. However, the standard k-means clustering package (from Sklearn package) uses ...
1
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1answer
21 views
Question related to symmetry in distances
My dataset represents products and evaluation of every product by users. E.g., I might have:
...
1
vote
1answer
381 views
linear decision boundaries of using the Euclidean distance?
How does Euclidean-norm in K-means cause it to have linear decision boundaries?
The Euclidean-norm is a non-linear measure between two points, then how does it make the boundaries till linear?
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0answers
92 views
Is the Normalized Information Distance (NID) a euclidean distance?
I'm trying to determine if I can plot my dissimilarity matrix (which is a Normalized Information Distance / the "Universal Distance Metric" dissimilarity matrix), in a lower-dimensional space using ...
2
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0answers
1k views
Convert Pearson correlation matrix into dissimilarity matrix [duplicate]
I would like to execute multidimensional scaling (MDS) based on a matrix of Pearson correlation coefficients. The sklearn.manifold.MDS function takes a dissimilarity matrix as an input and I therefore ...
0
votes
1answer
492 views
Is there a difference between K Nearest Neighbor = 1 and Minimum Euclidean Distance Classifier?
I believe the header asks it all. In this case, I am assuming I am using euclidean distance for KNN as well. Therefore when KNN = 1, I should be looking for only the nearest point, which should be the ...
