Questions tagged [unsupervised-learning]

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

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What should I be careful when using the word “supervised” in paper writing?

I am a biologist using machine learning tool for my research. I modified matrix decomposition ($V \approx WH$) to fit my data and wanted to describe about that in my paper. If I fixed one matrix ...
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9 views

Why grpreg library and gglasso library are giving different results for group LASSO? [closed]

I have been trying to do unsupervised feature selection using LASSO (by removing class column). The dataset includes categorical (factor) and continuous (numeric) variables. Here is the link. I built ...
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11 views

Intertia significantly decreasing after running multiple k-mean clustering models

I have been spending some time running k-means clustering models using scikit-learn on a variety of feature combinations and have been using the inertia value to compare models to one another. I've ...
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30 views

Missing data in k-means cluster model

I'm working on clustering email addresses using K-means based on their value to and engagement with the company (metrics such as % of emails opened, # of web browsing sessions, etc). I would like to ...
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17 views

Preprocessing on unsupervised learning

I am working on a high dimensional problem that evaluates code readability according to specific metrics. The problem is that there is no 'ground truth' so I need to implement clustering (instead of ...
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13 views

Productionize (applied) PCA to detect outlier etc. long term with new data

I was wondering how one could use PCA in e.g. a dashboard for non Subject Matter Expert. For example, you are quite certain that 2 PCs are sufficient based on the current data. It also makes sense ...
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9 views

Is kinship detection limited to classification / unsupervised learning?

There are numerous studies and ML approaches published around kinship detection. Generally, the system is presented a pair of inputs (e.g, 2 photos) and a score is produced, enumerating the degree of ...
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12 views

What approach or unsupervised methods can be used to pick out patterns in noise?

This is a hypothetical situation. Let's say you have access to a lot of human behaviors and characteristics (features). Let's say you have a sample of 10000 humans. You know that within this sample, ...
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1answer
23 views

Kernel selection for one-class SVM learning

Has anyone seen compelling research on kernel selection for one-class SVM learning? I've not tracked this work in some time and am wondering if there's new work I've missed, particularly from the ...
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Are there any methods to detect whole multivariate time-series as anomalous from a set of multivariate time-series?

Consider a scenario with Dataset D as {T1, T2, ..., Tn} and Ti is a multivariate time-series of length mi as {X1, X2, ..., Xmi}. Here each record of the time-series Xi is a vector of attribute values {...
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20 views

To combine dataset or not to combine dataset in unsupervised clustering?

I have datasets of N different machines of the same type, for example: N = 1000, type = BMW i3. On these datasets I run a unsupervised clustering algorithm. My question is now: Does it make more ...
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How does one calculate maximum dissimilarity?

I created this test Dataset data <- data.frame(Gene1 = c(1,2,1,8,9,7), Gene2 = c(5,6,6,3,4,4), Class = c("Male", "Male", "Male", "Female", "Female", "Female")) ...
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23 views

Dimensions Of The Covariance Matrix

I know that PCA can be obtained by eigendecomposition of the covariance matrix, and the covariance matrix $S$ is obtained by the equation: $S = X^TX $, where $X$ is the centered data matrix. But I ...
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31 views

How to express the PCA principal components as a linear combination of input data?

In this paper in equation 1 it shows that the principal component vectors are the eigenvectors of the covariance matrix and gives the following equation $$\lambda_l\psi_l=C\psi_l$$ where $C$ is the ...
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1answer
42 views

Can t-SNE be directly used as a clustering algorithm?

I've been working on a dataset about a few millions commuters and their travel patterns with around 50 dimensions. And I'm applying t-SNE to the dataset. My initial goal of applying t-SNE was to ...
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31 views

Does it make sense to use feature selection methods to reduce dimensionality for unsupervised clustering?

If I have a dataset that is labeled with positive and negative examples, and I'd like to cluster (i.e. unsupervised) only the positive examples, does it make sense to reduce dimensionality using ...
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14 views

How to perform a classification model on clusters derived from cluster analysis

I'd like to compare whether classification models using a clustering technique before classification gives better predictions than classification models without clustering. Quite similar (but more ...
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18 views

From unsupervised to supervised in fraud detection

I have a question. I am working on the fraud detection domain. And I have data from imports to the country. As you can get from the title, I have unsupervised data. I do not know that the record is ...
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1answer
46 views

Why do we compute eigenpairs of $X^TX$ instead of $X$ in Principal Component Analysis?

What is the intuition behind finding the eigen-vectors and eigen-values of $X^TX$ or $XX^T$ instead of the matrix $X$? One reason that I see immediately is when the matrix $X$ is non-square. I am ...
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22 views

Subsequent data transformation in Deep Belief Networks

I have a Deep Belief Network made of 4 Restricted Boltzmann Machines. The lowest level RBM seems to train correctly. The deeper layers however seem not to learn anything at all. I plot the receptive ...
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1answer
37 views

What is it called to cluster some inputs, then classify other inputs into those clusters?

I am learning about the problem of whole-book recognition, which is tangential to optical character recognition. Some of the strategies used to identify printed characters rely on first unsupervised ...
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16 views

Cluster Events of specific time window

i specify a timewindow of some minutes and extract occurences of certains events. The resulting matrix will look like this: ...
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15 views

Finding relations between events in a specific time window

i have following problem: I have a system containing several modules that, upon occurence of a problem, all send an event (containing an error code, time (is inaccurate thats why not really useful), ...
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15 views

What's the good index to choose number of clusters so that obtained clusters are homogeneous?

I perform a clustering on one-dimensional dataset and I need a way to automatically decide what's the optimal number of clusters from $k \in \{2, 3, 4, 5, 6\}$. The number of observations to cluster ...
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1answer
22 views

Are there any clustering techniques that work well on galaxy arm dataset?

Are there any clustering techniques (k-means, GMM) that work well for this dataset?
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Can someone verify if the following Bayesian Information Criterion (BIC) model selection algorithm is correct for Gaussian mixture models?

I am trying to find an automated way of picking the number of clusters $K \in \mathbb{N}$ for unsupervised learning scenarios, specifically for GMM. I was suggested to use something called the "...
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1answer
35 views

Clustering Proof of Equation

Greetings! Could anyone enlighten me about the validity of this equation? I'm trying to prove it without success. $K$ is the number of clusters, $C_i$ is the $i$-th cluster, $m_i$ is the number of ...
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25 views

Why do we get a new co-ordinate system when we dot product the transpose of eigen vectors with the transpose of a matrix

I am working on implementing PCA on the MNIST dataset and have calculated the eigen vector and eig Values from the co-variance matrix. Now I want to have a new co-ordinate system represented by PC1 ...
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35 views

For PCA, the best axis for the projection of the points is the one who has the minimal or the maximal inertia?

I am learning statistics mainly from a book : Elements de statistique, from three author of the Brussels University. 5th Edition On the PCA chapter, however, I need to read from other sources, ...
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1answer
16 views

How to unsupervised-cluster of binary vectors?

I have a set of binary vectors of roughly 500 dimensions. For EDA purposes mainly, I'd like to cluster them, maybe hierarchically. What could be the right distance metric for my problem? Is the ...
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1answer
15 views

Interpretation of Clustering Results

We are trying to use a clustering technique to isolate and analyze bugs when the software is in use in the production system. Here, features of the data are whether a user used certain features/...
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5 views

Effect of multi colinearity on machine learning problems other than regression

High colinearity between variables/ features is a problem when peforming regression analysis. But does the same stands true for Classification, ...
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1answer
30 views

Why do we penalize individual example divergence in variational autoencoder?

In variational autoencoder, we want to learn a mapping between input space $X$ and latent space $Z$, and $z\in Z$ is related to $x\in X$ with $z\sim MVN(\mu(x), \Sigma(x))$. In addition, we desire ...
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33 views

Combining continuous and binary data in unsupervised learning

I am working on cluster detection in a data set consisting of housing data. Each data point has some continuous features, such as house size, and some discrete ones, such as the number of garages (0 ...
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1answer
28 views

How do you train a model on a dataset that's unlabeled but we know the percentage of every class?

Say we have a data set that's pictures of apples and oranges, but we don't know which is which. However the data is organized in such a way, that for some groups of images we know how many of them are ...
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88 views

Why am I getting accuracy of 100 percent using SVM

I am working on Credit card data set for fraud detection. When I apply SVM for it, I am getting the accuracy as 100 %. What might be going wrong here? Here is the code ...
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Clustering binary data : feature selection vs Apriori

My data set is a 999 rows x 964 columns Panda DataFrame. Each row is a user. My data is binary : 0 for absent and 1 for present. I would like to fit the users ...
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27 views

ICA with a Laplace distribution

It's pretty common to set up ICA with a logistic distribution, but how would you find the loss and gradient with a Laplace distribution?
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43 views

Why does `sklearn`'s validation curve return test scores in unsupervised learning?

I'm using sklearn.model_selection.validation_curve to determine $k$ for my $k$-means clustering. For inputs, the y parameter is ...
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27 views

Differences Between Pearson'S Correlation And PCA Biplots

I have a scaled PCA biplot of the first two PCs of a data set with 43 observations and 5 variables. The biplot indicates certain relationships between variables, based on the angles between the ...
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Measuring the variance of an unsupervised model

I have a combined model that consists of two unsupervised models, an auto-encoder and a K-means clustering model. The combined model is showing variance in its prediction and I was not sure how to ...
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What are the applications of RBM and why do we choose RBM for them?

I was wondering what the applications of RBM are. In addition, why do we choose RBM in each of those applications. For example, in some cases both RBMs and auto-encoders can be used, but we may ...
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1answer
18 views

Unsupervised Learning in instances where the researcher determines whether a variable should be small or large?

Sorry if the title is confusing. Essentially, supposed I have two variables; height and weight. My goal is to find the individual who is equally very tall and very skinny (low weight). The idea here ...
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280 views

How to differentiate Auto Encoder techniques from Self Supervised Learning?

Auto Encoders(AE) learn a compressed representation of raw data by trying to reconstruct the input from the hidden representation. On the other hand, Self Supervised Learning(SSL) algorithms learn on ...
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Why generative models in Machine Learning are Boltzmann distribution-backed?

I learned from this review paper that MaxEnt models naturally display a Boltzmann distribution for the data samples, it comes from the Principle of Maximum Entropy. But I could not understand why this ...
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1answer
120 views

Why is k correlated with the mean and variance of the distance between centroids in k-means?

I've noticed that if I'm doing k-means clustering (in MATLAB) on basically any set of data (not randomness), the mean and variance in centroid linkage distance appears to always be approximately ...
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1answer
21 views

Function of Feature Transformation using PCA

I completely understood the math behind PCA. I have a doubt here while calculating the function that will do the transformation. According to the book : Deep Learning by Ian Goodfellow, Yoshua Bengio ...
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Error in flexmix gaussian mixture model (all components removed)

I am running gaussian mixture models with flexmix R package. The function works fine with up to k=20, but when I try k>20 it gives the following error: error in flxfit(model = model, concomitant = ...
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92 views

Can Agglomerative Clustering (Hierarchical) form non-convex clusters?

I want to know whether Agglomerative Hierarchical clustering draws non-convex cluster boundaries. From sklearn's comparing diff clustering algorithm experiment it seems like Agglomerative clustering ...
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Algorithms or techniques for pattern recognition [closed]

I am looking for algorithms or techniques that could be used for pattern recognition/extraction in data. Preferably I would like to know if there are any algorithm implementations in Python that I ...

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