Questions tagged [cosine-similarity]
An angular-type similarity coefficient between two vectors. It is like correlation, only without centering the vectors.
108
questions
0
votes
0answers
9 views
How to improve facial recognition using cosine similarity
I'm using pretrained vgg16 model for feature extraction and then using cosine similarity to compare 2 embedding more like Siamese network. It gives descent results, above 60% for the true match and ...
1
vote
0answers
25 views
Euclidian distance vs cosine similarity
Currently I'm working on facial recognition. If I use encoding/feature vectors of 2 images which method will prove more accuracy, L2 norm or cosine similarity and why?
I read "ICA performs ...
0
votes
0answers
18 views
logic behind two variables correlated at .99 but correlated differently to a third variable
Dataset has > 3000 observations. Each observation includes vectors A, B, and C. I compute the following new variables:
Variable sim_B.C is coding the cosine similarity of vectors B and C
Variable ...
0
votes
0answers
12 views
Metric for comparing cosine similarities to values in range(1, 5)
I am using cosine similarity as a metric for the semantic similarity of sentence pairs.
...
0
votes
0answers
19 views
What's the advantage of cosine distance over Jaccard distance for text document similarity
We usually use cosine distance as the similarity measure for text document. However, Jaccard distance also somehow make sense to me.
My question is for text document, is there any advantage of using ...
1
vote
1answer
21 views
Cosine similarity for Categorical datasets?
Can I use Cosine similarity measure for estimating similarity/relationship between D1 and D2 (two categorical datasets)
0
votes
0answers
20 views
An valid evaluation metric for recommender system
Good evening.
I wonder if you could help me with this?
I'm working on an item-to-item recommender system.
My data is a matrix, where columns - items, rows - users.
The goal of the algorithm is to ...
0
votes
0answers
13 views
How to calculate similarity between two sets of items rated on a single dimension?
(I'm just making up variables for this example.)
Let's say I have 100 words rated on their pleasantness.
I also have 100 images rated on their pleasantness.
I then had participants rate the fit ...
1
vote
0answers
27 views
cosine of angle between random variables is equal to the correlation coefficient? [duplicate]
I have seen this said multiple times where (1) the cosine of the angle between the random variables (on a vector space) is equal to the correlation coefficient, and (2) the claim if random variables ...
1
vote
1answer
20 views
Difference between an interaction and combining two variables into one with a similarity measure?
Conceptually, what would be the difference between interacting two variables in a regression and combining these two variables into one via a similarity measure? I'm looking at top managers and boards ...
0
votes
0answers
25 views
Performance of model decreases when calculating Euclidean distance between vectors with TF.IDF weigths compared to TF weights
My problem in short:
I use Jaccard similarity, cosine similarity and euclidean distance to compute a similarity between documents. The documents consists of either the words,hashtags or combination of ...
0
votes
0answers
30 views
How to calculate the similarity of data with noise?
I'm stuck on calculating similarity. Please tell me in which direction to move.
There are three files of different lengths that need to be compared for similarity.
It is supposed to use the cosine ...
0
votes
0answers
8 views
How to manage the influence of variables for lsa cosine?
I'm building reccomendation system for movies and the following data
...
0
votes
0answers
23 views
Is a normalized cosine similarity a bregman divergence?
A Bregman divergence is defined as
$D(p,q) = F(p) - F(q) - < \nabla F(q), p-q>$
with F a strictly convex function of the Legendre type.
Squared Euclidian distance is a Bregman divergence, with $...
0
votes
0answers
11 views
How to estimate the convolutional representation of a graph from its similarity to other graph convolutional representation?
Suppose we have two graphs A and B disconnected to each other (let's say 2-hops each), within a larger graph. If the Convolutional representation of graph A is known, is it possible to estimate the ...
0
votes
0answers
19 views
Calculating similarities between two populations using embeddings
I would like to find items from population B that are most similar to an item from population A. I have the following set up:
Two sparse datasets where each row is an item (treat row index as item ID)...
0
votes
0answers
21 views
What are the conditions for 2-dimensional curve fitting when the estimator function is a type of sine and cosine?
What are the conditions for 2-dimensional curve fitting when the estimator function is a type of sine and cosine?
For example I wanted to estimate F=xy with Asin (((n*pi)/L)*x)sin ((((npi)/L)*z) ...
1
vote
0answers
29 views
Which text similarity algorithm should I use to compare the context of Instagram hashtags?
For a study I am comparing companies based on the posts written by their Instagram followers. I apply the following technique:
Nike has 1.000.000 followers.
2000 random followers of Nike are selected ...
2
votes
1answer
29 views
Should we apply PCA before calculating similarities in high-dimensional space if my observations have length 1?
I have high-dimensional space (around 20 features) and I want to calculate similarity based on the angle of observation, not the magnitude. I have a nice function that can compute euclidean distance ...
0
votes
0answers
123 views
Can a cosine similarity be high while a Pearson correlation be low for a pair of vectors?
I was reading a paper on neuro-evolution related deep learning paper.
In that paper, the authors showed the Pearson correlation between the true gradient vector and the evolutionarily estimated ...
3
votes
0answers
33 views
Normalized Levenshtein distance and triangle inequality
One question regarding to the triangle inequality of normalized Levenshtein Distance. I use the well-known form D(X,Y) = 1 - d(X,Y) / MAX(|X|,|Y|) where ...
0
votes
0answers
153 views
Right way to use Cosine Similarity in R using TermDocumentMatrix
I have a TermDocumentMatrix of ~500 documents, with ~90 terms as some kind of train set. I want to implement a Cosine Similarity function, to classify a new document. It is short documents(messages), ...
1
vote
1answer
71 views
Rao-Stirling diversity index
I need help to calculate the Rao-Stirling diversity index. I tried it several times but cannot achieve equal results to R packages yet (e.g. diverse).
I used the Rao-Stirling diversity index as ...
0
votes
0answers
24 views
Cosine Similarity for Classification to EM Cluster?
Perhaps my question sounds naive, uncovering the very little knowledge that I have in the field of Statistics, but is very urgent to get a solid answer or trigger for further insights for my concerns.
...
0
votes
1answer
26 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 ...
0
votes
1answer
64 views
Cosine similarity for recommendation systems
Recently picked up recommendation systems and was going through User Based Collaborative Filtering(UB-CF).
Somewhere in the text, it specified that cosine similarity is one of the measures to find ...
0
votes
1answer
61 views
Why is the cosine similarity between these (seemingly uncorrelated) vectors so high?
I am calculating the correlations between vectors of experimental data by a variety of methods (Pearson's, cosine similarity, Euclidian distance, etc.). Most the results look fine, but occasionally ...
0
votes
1answer
76 views
Cosine similarity vs quadratic objective function
The aim is to calculate the similarity between two foods given the nutritional content of each. After some reading, it seems the most popular measure for this sort of problem is the cosine similarity ...
0
votes
1answer
136 views
Does it make sense to normalize vectors after PCA for cosine distance?
I start off with word2vec embeddings and process them in the following way:
Standardize dimensions to mean 0 and standard deviation of 1
PCA to keep the top k-dimensional eigenvector, whereby ...
1
vote
0answers
21 views
Deep item-based recommender objective function
I'm trying to understand the following paper written by researchers at eBay that uses deep learning to overcome the problem of making recommendations when you mostly have one-of-a-kind items. A ...
0
votes
0answers
143 views
Extension of the K-Nearest neighbor algorithm to get results in different neighborhoods
I would like to use the kNN algorithm to find the closest neighbor to a vector. But I would like it to limit to a point per neighborhood (radius)
In this image, given the point in red, I would like ...
1
vote
0answers
155 views
Should I normalize the vectors by row or by column before performing cosine similarity?
I have a dataset which contains vectors of different features that generated from subtitles in movies, something like:
...
1
vote
0answers
114 views
How can I define a accuracy measure for word2vec predictions
I have a data set consisting of tags and some classes.I'm suppose to find the nearest class to each set of tags with Word2vec embeddings and cosine similarity.Each set of tags have multiple classes ...
1
vote
0answers
38 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 '...
0
votes
0answers
22 views
analyze time-series consine similarity matrices (in R)
I have calculated the cosine similiarity between multiple users for 10 years separately.
I can visualize and cluster the matrix of each year but I was wondering if there is a smart approach, vignette ...
2
votes
0answers
72 views
Are there advanced “cos similarity” that influence of dim size is less?
I found that the cosine similarity is
affected to the effect of "Curse of dimension" by trying the following simulation.
create(select) two vectors form uniform random numbers U[-1, 1], each dim = 2,...
0
votes
1answer
23 views
Did my text data come from two distinct distributions?
I have labeled text data from two different classes. I have calculated tfidf feature representation of all the sentences in question. I have a huge matrix where rows are sentences and columns are ...
0
votes
0answers
97 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 ...
0
votes
0answers
31 views
Cosine similarity matrix of linearly transformed inputs
Given a matrix $\mathbf{C}$ which contains pairwise cosine similarities between rows of a matrix $\mathbf{A}$, linearly transformed by matrix $\mathbf{U}$:
$$
\mathbf{C} = K(\mathbf{UA}, \mathbf{UA})
$...
2
votes
1answer
68 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 ...
1
vote
1answer
545 views
Finding similar text - algorithms and evaluation
I've been asked to create a program that will rank similar texts to an input text given a collection of text.
So far I've been using a tdidf representation and cosine similarity with a lot of regex-...
1
vote
1answer
296 views
Calculating similarity between two lists: high cosine similarity, but high RMSE
I want to see how similar two datasets are, as a way to justify that they can be used in similar contexts. In practice one dataset contains manually calculated data, and the other automatically ...
5
votes
1answer
2k views
Weighted Cosine Similarity
To convert cosine similarity to weighted cosine similarity, one can use at least two approaches. But I don't know which one is better.
The first approach is to first reweight each vector and then ...
1
vote
1answer
239 views
Spectral Clustering of a skipgram model
I have a model where I'm applying Spectral Clustering to frequencies of words. My pipeline consists in TF-IDF, followed by a <...
0
votes
1answer
179 views
Text Similarity - Cosine - Control. Suggestion to another / better method?
I would like to ask you, if anybody could check my code, because it was behaving weird - not working, giving me errors to suddenly working without changing anything - the code will be at the bottom.
...
0
votes
1answer
47 views
Similarity index between two texts Ask Question
I'm trying to compare two vectors in a small NLP project using Python. Code doesn't make any difference since I'm using scikit-learn, but my doubts are about my calculations.
I have a query vector ...
2
votes
2answers
306 views
How to fit laplace/exponential distribution to cosine similarities?
I am a computational biologist with little experience fitting data.
I'm trying to fit a distribution of cosine similarities computed between sparse matrices. The goal is to be able use this ...
0
votes
0answers
74 views
How to create a binary threshold from Cosine distance between 1-D arrays?
I have a graph of the Cosine distance between the question and the sentence most similar to it when there is an answer and when there is none.
I want to establish a threshold on the abscissa axis ...
1
vote
2answers
910 views
Cosine-Similarity vs non-linear measures
In NLP, people often use cosine similarity to measure how close two vector spaces are to each other. However, we know that cosine-similarity is the same thing as Pearson correlation, for centered ...
0
votes
1answer
188 views
K-means for data sets with scalar and vector objects
My question consists of two parts, both possibly closely related:
Part 1:
I have a dataset where the incoming data ($x$) will be an eigenvector ($V$) and an associated eigenvalue ($\lambda$). That ...
