Questions tagged [euclidean]
Euclidean distance is the intuitive notion of a 'straight-line' distance between two points in a Euclidean space.
5
votes
1answer
135 views
Why Kullback-Leilbler divergence is a better metric for measuring distance between two probability distributions than squared error?
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-...
0
votes
2answers
29 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 ...
1
vote
1answer
36 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
votes
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
vote
1answer
20 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 ...
0
votes
0answers
11 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 ...
3
votes
0answers
29 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 ...
0
votes
0answers
26 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 ...
2
votes
1answer
68 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 ...
1
vote
0answers
97 views
How to improve Pairwise Euclidean Distance for Similarity Measure
I am trying to identify the most similar stations between two DataFrames like below:
...
0
votes
0answers
45 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 ...
2
votes
1answer
212 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 ...
0
votes
0answers
96 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
votes
1answer
40 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
votes
1answer
51 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
votes
1answer
28 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, ...
0
votes
0answers
12 views
Comparing relative distances between separate ordinations
I have four sites:
Treatment_1 and Control_1 of experiment 1 (8 plots in total)
Treatment_2 & Control_2 of experiment 2 (8 plots in total)
I ran an RDA for each experiment separately to compare ...
0
votes
0answers
17 views
Best method to obtain representative sample for clusters in high-dimensional space
I am clustering a large amount of high-dimensional data using KMeans (and the Euclidean distance metric), and then calculating the silhouette score and the Euclidean distance to the calculated cluster ...
-1
votes
1answer
347 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
votes
1answer
30 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
vote
1answer
124 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 ...
0
votes
0answers
7 views
What features or architecture for a text answering system?
From this dataset with paragraphs, questions about these paragraphs and answers from these paragraphs, when there exists, I'm trying to predict the sentence where there is an answer. After ...
9
votes
1answer
468 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
vote
1answer
44 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
vote
1answer
26 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
votes
0answers
244 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 ...
0
votes
0answers
71 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
318 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
votes
0answers
57 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) ...
1
vote
0answers
21 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
votes
0answers
112 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
votes
1answer
106 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
vote
0answers
398 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
votes
1answer
535 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.
3
votes
1answer
2k 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
votes
1answer
145 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
150 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
votes
0answers
227 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
35 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 ...
1
vote
0answers
293 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 ...
7
votes
1answer
11k 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 Kmeans clustering. However, the standard Kmeans clustering package (from Sklearn package) uses ...
1
vote
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
310 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?
0
votes
0answers
85 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
votes
0answers
964 views
Convert Pearson correlation matrix into dissimilarity matrix
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
454 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 ...
2
votes
0answers
985 views
Cross-validated Manhattan/L1 distance
Consider the task that we want to compute the Euclidean distance between two vectors $\mathbf{a}$ and $\mathbf{b}$, where the vectors are noisy sample from some measurement. Our goal is to get an ...
2
votes
0answers
390 views
Curse of dimensionality for time series?
I am a bit boggled when trying to conciliate econometric and machine learning view for time series.
Let's assume that each of the $N$ (stochastic) time series have $T$ synchronous i.i.d. observations,...
0
votes
1answer
452 views
High dimensional clustering of percentage data using cosine similarity
I'm building a clustering algorithm and was trying to determine the best way to get separate and accurate clusters.
I have 300+ features to cluster on, and they are all percentages between 0 and 1. ...
1
vote
1answer
235 views
Comparing distance of two matrices with different sizes using eqdist.etest function in R
I want to compute a test statistic based on the Euclidean norm of two data matrices with same number of columns (i.e variables) but very different number of rows (i.e observations).
I am using the ...
