Questions tagged [unsupervised-learning]

Finding hidden (statistical) structure in unlabelled data, including clustering and feature extraction for dimensionality reduction.

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How to Define Equipment Churn in Laboratory Service Data Without Explicit Churn Labels?

I'm working with a comprehensive dataset spanning 20 years of service records for laboratory equipment owned by various customers. This dataset captures intricate details, such as the equipment ID, ...
tlengman's user avatar
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What is meant by the assumption of statistical independence among sources in Independent Component Analysis?

One of the underlying assumptions of independent component analysis (ICA) that I consistently see written is "statistical independence across the source signals". In the context of the ...
S.C.'s user avatar
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ML Clustering with an added condition

Problem: I want to create distance-based clusters of customers where each cluster, in sum, yields the same revenue potential. Explanation: I'm looking at thousands of customers spread throughout a ...
Tommy Lee's user avatar
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1 answer
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Using unsupervised methods prior to cross-validation when all unlabelled data is available

There is lots of discussion about pre-processing methods and if they need to be included within a cross-validation procedure or if they can happen prior to splitting the data -- questions on ...
A. Bollans's user avatar
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4 answers
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Why does Harrell Argue for "Ignoring Y during data reduction"?

In Regression Modeling Strategies page 79 (4.7 Data Reduction) reads: Data reduction is aimed at reducing the number of parameters to estimate in the model, without distorting statistical inference ...
purple-blade's user avatar
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Adjusted Rand Index Per Single Cluster

i have a nuanced ARI question for which i can't find any reference. say i have two clusterings, say ClusteringA and ClusteringB, and i am comparing them using ARI. now, i'm not interested in the ARI ...
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How to validate unsupervised anomaly detection in absence of ground truth?

I am currently working on an unsupervised anomaly detection project and facing a challenge regarding the validation of the model's performance due to the absence of ground truth labels. I am using ...
Camilo Piñón's user avatar
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Difference between Principal Component as closest linear surface and regression line as best linear approximation in two-dimensions

I am currently working on a presentation on PCA as part of a seminar programme and one slide is about giving an interpretation on the first constructed Principal Component on an arbitrary two-...
LibraryWarden's user avatar
4 votes
3 answers
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Outlier detection methods aware of target variable

I am trying to predict ambulance demand for the next hour, for a city area in the USA, based on previous demand, weather, large people gatherings, and similar spatio-temporal factors. I have noticed ...
Nadir Bašić's user avatar
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How to perform Hierarchical Clustering using centroid method and custom distance metric?

I would like to perform Agglomerative Hierarchical Clustering using the centroid method (defined on this page) and a custom distance metric, probably cosine similarity. In the Scipy docs it says you ...
Rupert Hart's user avatar
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What method(s) answer question 'should X be a category'?

To some degree my problem may be lacking terminology to ask the question well. Form of a problem I'm looking at is as follows: some number of subjects answered (luckily in a binary way, that is: 'yes',...
Teleoflexuous's user avatar
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Why is HDBSCAN considered a transductive clustering algorithm despite having approximate_predict?

In the documents for HDBSCAN it says the following: Often it is useful to train a model once on a large amount of data, and then query the model repeatedly with small amounts of new data. This is ...
TunaFishLies's user avatar
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Looking for a way to train a model to learn optimal parameters/hyperparameters of clustering

I have 5000 docs, each is a review. For each review, I'm plotting the sentences in a semantic dimension. Now, I'm applying clustering to these points for each review. The success of my model depends ...
Prithvi's user avatar
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When to use Mixture Models (e.g., Latent Class Analysis) vs. Cluster Analysis (e.g., K-Means) for segmenting subpopulations?

I have watched a video describing the differences between Cluster Analysis and Mixture Models. https://www.youtube.com/watch?v=HwsMZwhO7wU&t=2s Clustering determines compact clusters and assigns ...
JElder's user avatar
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What's the best clustering algorithm for Fraud Data?

Background I'm working on a Fraud dataset with 500,000 samples, and 130 features. There are: 98 numerical features, 32 categorical features, There are missing values in: 7 numerical features, 12 ...
Connor's user avatar
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What is the point of finding the best matching unit in a self organizing map?

During the update step while training a self organizing map, the best matching unit for a particular input is found, then that BMU (and to a lesser degree it’s neighborhood) is perturbed to better ...
Laura's user avatar
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Examples of unsupervised neural networks that are not self-supervised

I'm trying to understand if such things exist. For the NN to learn it seems that we need a measure of error and thus we need labels. In the case of auto-encodoers, which are considered unsupervised, ...
marcelo antunes's user avatar
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'City climate twins': similarity measure for monthly temperature data

Cities are getting hotter due to climate change. I want to communicate this intuitively through a concept of 'city climate twins': ie. which other city will my city's climate resemble several decades ...
Nicholas Jones's user avatar
2 votes
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How to prove that 2d support vectors are enough for Hard Margin Linear SVM?

As the question states, how can I prove mathematically that 2d support vectors are enough to always be able to formulate the Maximum Margin Hyperplane in d dimensions?
heloworld's user avatar
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Toy dataset: Radial VAE

I'm evaluating disentanglement in toy datasets seeing as we have such little understanding of the phenomena. I'm using various tools from differential geometry. Now I want to train a VAE on the ...
John Miller's user avatar
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1 answer
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Deriving a boundary for a single cluster data

Summary I have a dataset of 2D points that exhibit a distinct pattern. My goal is to create a boundary that encompasses the main cluster of points while excluding outliers. In other words, if a point ...
Victor Yerz's user avatar
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In unsupervised learning, is a result of 2 clusters meaningful?

I used both agglomerative clustering and k-means on a dataset and see the results below. Result from agglomerative clustering was demonstrated with silhouette score while kmeans with inertia score. ...
LCheng's user avatar
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LMest for cluster membership over time

I have a dataset of 18 continuous variables measured over 3 time points for 90 patients. I hypothesise that there are clusters of patients with similar characteristics and that cluster membership may ...
HarD's user avatar
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3 votes
1 answer
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How is the "training error" of KNN plotted?

On https://www.cs.ubc.ca/~murphyk/Teaching/CS340-Fall07/L4_knn.pdf Page 6 The author (Kevin Murphy) plots the training and test errors associated with kNN classifer. I am not sure how training error ...
Fraïssé's user avatar
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Approximate Gower's dissimilarity measure

I have a very large dataset with mixed-type variables. When I apply the Gower's dissimilarity measure to obtain the distance matrix, it is running out of memory. Due to the large size of the data, it'...
Phoebe's user avatar
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Variable selection - Decide for family, link, etc. after or before?

This may be a dumb question but I was wondering about the appropriate chronology to adjust model parameters in a glm when performing variable selection. Suppose, one has a set of predictors $x_1$, $...
a.henrietty's user avatar
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Measuring similarity between two datasets

This is quite straightforward: I have a dataset with 8 features containing cell measurements from a patient. I want to compare two patients to a reference one. To do this I perform UMAP to reduce the ...
amr95's user avatar
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Metrics for multiclass classification

Background Consider a clustering problem where a dataset of measurements $Z\triangleq\{z_i\}_{i=1}^m$ must be partioned in $n$ clusters, where $n$ is unknown and must be estimated. Here the term "...
matteogost's user avatar
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Hierarchical labelling for independent variable in training data

I have data where the objective is to use supervised learning to predict 4 different outcomes. Say the classes are 1, 2, 3, 4. Though discrete, they are also hierarchical, where class 2 is of an ...
Mikee's user avatar
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Regression with unlabeled data from several clusters

I want to characterize the relation of a few input parameters to a single output parameter. The problem I have is that my data is collected from several groups. The groups are defined both by the ...
Ohad Dan's user avatar
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Is there any reason why there appears to be not much modern research into self organizing maps (SOMs)?

Usually there are clear advancements in ML methodologies in research especially, where I can say X method is essentially better than Y method for most datasets. However, I recently accidentally ...
tisPrimeTime's user avatar
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Unstable HDBSCAN & UMAP clustering results

I've segmented my data using UMAP (dimensionality reduction, with a fixed random seed) and subsequently HDBSCAN to generate a number of clusters. I've also looped through different values to fine-tune ...
sara's user avatar
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unable to figure out why overlapped clustering is occuring

i am appling k means and DBSCAN to the same data set which is in csv format, but i'm unable to get good results. I have attached the DBSCAN results.I,m getting overlapped clusters and can't figure out ...
user387109's user avatar
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1 answer
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Deep learning classification with multiple temporal data

I'm working on a project to predict the category of music segments in an audio file (represented in pianoroll format with an additional column for the corresponding class). Each row represents the ...
Tria Ufo's user avatar
2 votes
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Understanding diagonal rescaling in multiplicative update rules for NMF

SUMMARY How does the diagonal rescaling fit into the derivation of a multiplicative update rule for non-negative matrix factorization (NMF)? DESCRIPTION The NMF problem aims to find non-negative ...
scho's user avatar
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Silhouette Score for ordered clusters

My clusters are arranged according to a time series, and I want to compute the silhouette score for the clustering performed, considering that they follow an order. Therefore the nearest cluster to ...
sp29's user avatar
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1 vote
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Learning based on correlations?

I am currently interested in a specific supervised learning sub-problem. We have access to Data X and targets y as in traditional statistical / supervised learning. However both X and y are very noisy....
Lucas Morin's user avatar
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1 vote
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Unsupervised Ranking

Assume we have N data points like data = [(x1, y1, z1), (x2, y2, z2),....., (x_N, y_N, z_N)]. I am looking for unsupervised ranking algorithms. The thumb of rules ...
TripleH's user avatar
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4 votes
1 answer
1k views

How to compare labels from clustering analysis and original ones?

I was asked to run a clustering analysis to assess the validity of labels for a manually labelled dataset. I can simply save the actual labels (4 classes: 0, 1, 2, 3) and run clustering analysis (let'...
AngelMarcos's user avatar
1 vote
1 answer
187 views

Unsupervised learning: How to identify differences between clusters?

I'm learning about unsupervised learning and I tried to use KMeans, AgglomerativeClustering and DBSCAN on the same datase. The result was ok, they seems to work fine according silhouette_score() ...
Antonio Caipora's user avatar
1 vote
1 answer
44 views

How can I draw the decision boundary for a simple competitive network?

I know how to train a simple competitive network. Let's say I have three inputs $x_1, x_2, x_3$ and learning coefficient $\eta=0.5.$ Let's say I have two neurons $w_1, w_2$. For each input I will ...
tonythestark's user avatar
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0 answers
26 views

Are web-scale deep learning models considered to be supervised or unsupervised?

Consider recent large-scale deep learning models, such as transformers for NLP applications, or diffusion models for text-to-image generation. These models are trained on huge, readily-available ...
John Rowlay's user avatar
1 vote
0 answers
37 views

Can K-means put most of the noise in the same cluster?

I am working on clustering text data (very short sentences) vectorized with tf-idf. The data are characterized by high sparseness and the presence of abundant noise (considered here as documents that ...
zurgo's user avatar
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1 vote
1 answer
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Do you use the PC1, PC2, PC3 or do you use PCA for feature selection in supervised learning? [closed]

My goal is to understand what is PCA for in supervised learning. Do we use the PC1, PC2, PC3 to the supervised learning? Do we use the generated labels to the supervised learning? If we use the PC1, ...
Jason Rich Darmawan's user avatar
1 vote
0 answers
37 views

Unsupervised many-to-one matching for two categorical variables

I have data on the sales of products sold at various retailers. My interest is in uniquely matching the product class to another arbitrary class. For example, for two sets of hypothetical classes: ...
Matthew Lowe's user avatar
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0 answers
21 views

A/B testing for channel preference

Right now my organization runs a lot of promotional campaign every month. We send email, SMS and WhatsApp to all customers for each campaign. I am running a project to identity the best channel for a ...
Subhashree subhadarsani's user avatar
2 votes
2 answers
2k views

Implementing cross-validation to tune the hyperparameters of an unsupervised model

I have extensively researched the application of cross validation for unsupervised learning (as it is a requirement by my project manager) but it seems that there is no clear consensus as to how to ...
Octave's user avatar
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1 vote
1 answer
264 views

Does Discriminator in GAN Train only on Real Data or it also Trains on Fake Generated Data

I have been studying GANs and I got confused in the training phase from the discriminator. Which I think only trains on Real data, not on the generated data which then helps in distinguishing or ...
Muhammad Wasil Shahzad's user avatar
2 votes
0 answers
37 views

Is there an improved canonical polyadic decomposition for symmetric tensors?

Let us suppose I want to find a CP decomposition of a $n$-mode tensor $\mathcal{A}$. Fortunately the tensor has the permutation symmetry $$\mathcal{A}[i_1, \cdots, i_n] = \mathcal{A}[\sigma (i_1), \...
Galen's user avatar
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2 votes
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Why use IsolationForest over other supervised methods for semi supervised learning?

I have a dataset with labels that I'm using to explore unsupervised learning (IsolationForest) with. IsolationForest has a few hyperparameters, and some can be heuristically determined like maybe you ...
Sky's user avatar
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