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Questions tagged [distance]

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For question regarding distance between distributions or variables, such as Euclidean distance between points in n-space.

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3
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4answers
260 views

Clustering algorithm which does not require to tell the number of clusters

I have a dataframe with 2 columns of numerical values. I want to apply a clustering algorithm to put all the entries into the same group, which have a relatively small distance to the other entries. ...
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1answer
20 views

Very basic question: what is an accepted term for “linear order distance”

In data science we have "Manhattan Distance" as a slang term for Level 1 Distance and "Euclidean Distance" as a slang term for Level 2 Distance. Is there an accepted term for linear distance in memory ...
2
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1answer
27 views

Cluster elements that appear in the same lists

Suppose I have a multitude of sets with (unordered) combinations of elements and I want to determine which elements tend to appear together. For example Given the following sets: ...
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0answers
22 views

How to have an insight of very similar processing methods by measuring the distance between observations produced by them? [closed]

Suppose that we use very similar methods(suppose now there are two such similar methods) to process upon each sample of a dataset. For each sample of the dataset with one of the two methods, we obtain ...
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1answer
26 views

Some robust perhaps more complex as neural networks to measure the distance between data? [closed]

Now after certain preprocess we can repeatedly get two sets of data points, exhaustively enumerate one from the first set and one from the second set to form pairs. Each pair of these two data points ...
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1answer
243 views

How to evaluate the K-Modes Clusters?

K-modes algorithm is available here I want to do clustering of my binary dataset. I need to specify the number of clusters that I need as an output: ...
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0answers
25 views

Voronoi Diagram Algorithm for Geo Locations? [closed]

As per my Understanding: Voronoi diagram: A Voronoi diagram is a simple concept, and it's based on the minimal distance needed to reach a landmark. If you need to go to a metro station, the most ...
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0answers
15 views

Random Forest proximity matrix with or without in bag samples

I'm running Random Forest classifications; and PCA plots based on the resulting proximity matrices are quite helpful to my analyses. However, I found a comment on the R ...
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0answers
10 views

How to quickly find min, max and mean distance between samples fitted in Nearest Neighbors?

I am familiar only with SciKit Learn implementation of Nearest Neighbors model where one can fit data and execute K-neighbors or radius queries. This model does not provide any statistical information ...
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2answers
154 views

Question about Similarity vs Dissimilarity Matrix

Right now, I'm working on a coming up with a similarity vs dissimilarity matrix for a set of data points for a clustering algorithm. My question is, if I want to use one of the many clustering ...
0
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2answers
31 views

Question About Coming Up With Own Function for Distance Matrix (For Clustering)

Right now, I am currently working on implementing a clustering algorithm with millions data entries with regards to game users for a mobile game. A lot of the features I plan on using are unique to ...
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0answers
13 views

Finding the closest neighbour of multidimensial data point

I have a test point with 15 attributes. I want to find the closest data point to this test point from 10,000 data points. I'm thinking using something like this: ...
0
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0answers
46 views

How to calculate Fuzzy C-Means problem by hand

I figured that this doubt next can interest another students like me and help others also that are trying to understand mathematically the fuzzy c-means mathematical mechanism already that some books ...
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1answer
43 views

It seems that the output of sklearn.metrics.pairwise.euclidean_distances is different to the formula on doc

The doc of sklearn.metrics.pairwise.euclidean_distances() gives this formula dist(x, y) = sqrt(dot(x, x) - 2 * dot(x, y) + dot(y, y)). Apply this formula to ...
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1answer
908 views

Implementing Mahalanobis Distance in Python [closed]

I am trying to implementing Mahalanobis Distance from scratch but I am getting an error- The formula for Mahalanobis Distance is- Now my code is- ...
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0answers
11 views

What options do I have for measuring similarities by using vectors generated from texts?

I have a data set which contains vectors generated from subtitles, I want to measure the similarity between each pair of the observation. Now I have tried L1, L2, cosine similarity and Mahalanobis ...
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0answers
23 views

Is it necessary to perform Z-score or Min-Max normalization on L1-normalized data?

I have a dataset which contains vectors that generated from subtitles and have been L1 normalised, I want to calculate cosine similarity & Euclidean distance, I thought it is better if I use Z-...
2
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1answer
107 views

values passed to user-defined distance function by KNeighborsClassifier is wrong

I have a data-set in which all features are binary and the class of each data-point is also binary. I am trying to use KNearestClassifier with a user-defined distance function as follows: ...
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0answers
32 views

CNN Architecture for Measuring Object Distances

I'm trying to use CNNs for infering object distances from an image. The input images correspond to states of a 2D game: Game states are not represented as images but as matrixes of observations. ...
0
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0answers
18 views

Is there any paper introduce an intuitive method for clustering evaluation?

I would like to use the most intuitive method like minimizing the within-cluster distance and maximizing the distance between neighbouring clusters, but not sure does this method have a name or ...
3
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1answer
42 views

Is the distance in Nearest Neighbors a good measure of similarity?

Let's train a Nearest Neighbor model with just one sample in it: In [48]: nn = NearestNeighbors().fit([[0, 1, 0, 0]]) So this one sample has just one significant ...
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0answers
229 views

Finding linear transformation under which distance matrices are similar

I have n sets of vectors, where each set S_i contains k vectors in ...
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0answers
79 views

Cluster based on both positions and similarity scores

I have a dataframe position giving me the x and y positions of 87 points. I also have a 87 x 87 similarity matrix giving me the pairwise similarity scores between ...
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2answers
43 views

Estimating time to travel between two lat/longs

I'm trying to create an offline estimator for how long it would take to get from one lat/long to another. Two approaches I have come across are the Haversine distance and the Manhattan distance. What ...
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2answers
40 views

Reverse engineering a distance metric from the output of a k-NN

Suppose that someone has trained a nearest-neighbor algorithm based on some unknown metric. I have a large dataset of $N$ observations and $P$ features. For each observation, I am given $K$ indices ...
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0answers
31 views

Features Vectors in embedding space

I have a bunch of users, each of them with about 100 features. My goal is to create an embedded space to compute the "distance" between users. Also, I want to be able to visualize it with Tensorboard (...
4
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6answers
197 views

Distance between users

I want to compute the "distance" between users in order to return the top n similar users, for any given user. For each user a have a bunch of features. This is close to a recommendation system, ...
2
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1answer
97 views

Coordinate System's influence on $L$ distances (Manhattan and Euclidean)

I don't understand this picture, which says if we change the coordinate system, we would have the same result for $L_2$ distance, whereas, our result would differ for $L_1$ distance. What does it ...
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0answers
206 views

Implementation of Bhattacharyya distance for filtering images that are far off from their cluster

I need assistance with the python implementation of Bhattacharyya-distance for the below use case: Here, the polygons P1, P2..Pn refer to the different images where each pixel is represented by 'n' ...
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0answers
41 views

What is divergence exactly in machine learning?

I know about KL divergence, JS Divergence and clearly know that it is different from the divergence in calculus. I have an intutive feeling of divergence as it roughly compares the closeness of two ...
2
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1answer
287 views

Measuring distance preservation in dimensionality reduction

I am looking to compare the distance preserved during dimension reductions for several techniques. I have read some papers on similar topics here and here. For example, I would like to use the ...
3
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1answer
329 views

K Nearest Neighbour with different distance matrix to each datapoint

I'm wondering if there is library support in python (such as sklearn) for doing KNN on a data set that has a custom distance matrix (positive definite) for each data point (x is a query point, $x_i$ ...
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1answer
683 views

What does sklearn's pairwise_distances with metric='correlation' do?

I've put different values into this function and observed the output. But I can't find a predictable pattern in what is being outputed. Then I tried digging through the function itself, but its ...
0
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1answer
117 views

scipy.spatial.distance.mahalanobis return null values for some vectors in python

I am using scipy.spatial.distance.mahalanobis to calculate distance between two vectors but i'm getting null values for some vector I don't know why? null value is possible?
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1answer
544 views

How to measaure the similarity between two series?

I'm confused about how to measure the similarity between two time series with the same length. For example, both time series are 2 hours in length and every 5 minutes a point. I really want to know ...
0
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1answer
28 views

Date transformation for KNN to get distance [duplicate]

I have data set with date features like 01/01/2019 and I would like to use KNN. However, I cannot find a good transformation for dates that has a meaningful distance result for the last feature. For ...
0
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2answers
77 views

Date transformation for KNN

I have data set with date features like 01/01/2019 and I would like to use KNN. However, I cannot find a good transformation for dates that has a meaningful ...
2
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1answer
33 views

Positive semidefinite kernel matrix from Gower distance

I have a dataframe with continuous and categorical variables and I want to obtain a kernel matrix for classification. The kernel matrix must be symmetric and positive semidefinite, so that no ...
2
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3answers
85 views

Are there algorithms for clustering objects with pairwise distances, without computing all pairwise distances?

I'm looking for a clustering algorithm that clusters objects, by using their pairwise distances, without needing to calculate all pairwise distances. Normally pairwise clustering is done like this: (...
2
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3answers
1k views

Clustering algorithm for a distance matrix

I have a similarity matrix between N objects. For each N objects, I have a measure of how similar they are between each others - 0 being identical (the main diagonal) and increasing values as they get ...
2
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3answers
51 views

Match a two items from two different receipts

I have two different invoices or receipts. One is a Purchase order one is something like a receipt(acknowledgement). Suppose I have ordered(PO) Wine: White Wine Red Wine Rose Wine And I receive ...
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0answers
17 views

Which distance method will account for positions of observations in arrays?

I am trying to figure out the distance method suited for my problem. I have different arrays of the same length. Each position in the array matters. Let's say ... each array is representing an image, ...
2
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1answer
23 views

Is there a way to cluster words based on how similarly they sound?

I have a list of words for a fictional world I've made (don't judge lol). My ultimate goal is to generate more words that sound like them through a markov generator, but for now, I'm trying to build ...
2
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1answer
90 views

Clustering time series based on monotonic similarity

Context I am involved in a task of clustering 1500 time series of 500 observations into a few number of clusters. The time series share all the same observed property at different spatial locations, ...
3
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3answers
59 views

A metric between trees

I have certain tree structures. I am not an expert in machine learning. As I would with take KNN, I would calculate distances via metric function and a new data point and the points from the training ...
1
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1answer
480 views

Why don't we use Manhattan distance instead of euclidean distance in linear regression?

When I am explaining concept of linear regression to one of my peers, I got stuck in answer this question. Why don`t we use Manhattan distance instead of euclidean distance in linear regression? Can ...
0
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1answer
102 views

How can i use Hellinger Distance on array of different length?

I have to use Hellinger distance to compare arrays that are not the same length. How do you do this correctly? Putting a zero in the missing fields for the shorter array does not sound like the best ...
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2answers
396 views

How can I implement tangent distance for k-nearest neighbor in python/scikit-learn?

My ultimate aim is to have a function which I can feed into scikit-learn's NearestNeighbor class as a custom metric parameter. ...
1
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2answers
480 views

Nearest Neighbors on mixed data types in high dimensions

I would like to be able to use nearest neighbors to attempt to find the most similar samples to a subclass of samples (think treated vs untreated) in a dataset with continuous, categorical, and text ...
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0answers
9 views

How to generate multiple vectors given their distances [closed]

Well, the title says it all. I'm looking for an algorithm that would generate multiple random vectors with given distances between them. Any suggestions?

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