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Questions tagged [unsupervised-learning]

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

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PCA/Higher dim-data

If we relate the eigenvectors $A = (1/N)Y^T Y$ and $B = (1/N)YY^T$, how can I show that there's a matching between the non-zero eigenspaces of the eigenvectors ($A$ and $B$), so that if $\lambda$ ...
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How to interpret LDA (Latent Dirichlet Allocation)?

Say I want to run topic modeling with LDA on The 20 newsgroups text dataset. So basically a dataset with texts where every text belongs to one of 20 categories. I want the LDA to split the documents ...
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15 views

Inference can be the goal of an unsupervised learning method or a semi-supervised learning method or even more of a reinforcement learning method?

I am new to machine learning, and I am reading a pair of machine learning books. These references talk about 2 different learning approaches: Prediction and inference, I understand the difference ...
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1answer
9 views

Linking generative, discriminative models to supervised and unsupervised learning

Definitions that I am considering: A generative model learns p(x,y) whereas a discriminative model learns p(y|x=x). I would like to verify if my understanding is correct by sharing the following ...
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1answer
54 views

Scaling data with different importance

I have 9 attributes: x1,x2,x3,x4,...,x9 and I know that the attributes x9 must have the same value in a cluster and the attribute X1 have more importance than others (x2,...,x8) I'm using Euclidean ...
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2answers
35 views

Define attribute importance in unsupervised learning [on hold]

I'm using 'NbClust' package to help me to get the "optimal number of clusters" and I noticed in my dataset I have attributes with different importance. I have 5 attributes: x1,x2,x3,x4,x5 and I know ...
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3 views

Truncate a Hierarchical Clustering tree in order to get the cophenetic coefficient

I've gone ahead and clustered a dataset using a Euclidian Hierarchical Clustering algorithm: ...
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2answers
46 views

In k-means clustering, why sum of squared errors (SSE) always decrease per iteration?

In k-means clustering, why sum of squared errors (SSE) always decrease per iteration? How can prove it by mathematical derivation of formulas? k : number of clusters m : number of examples $c_h$ : ...
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1answer
53 views

Issue in evaluating the performance of my “clustering algorithm” using NMI, ARI when the “ground truth” is available? [duplicate]

(**Edited the question after the initial comments) Suppose, Ground_truth_data = [1, 1, 1, 1, 1, 1, 1]; Clustering_result = [1, 1, 1, 1, 1, 1, 2]; Here, as you can see, there are "7" instances of ...
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1answer
15 views

How to tune the hyperparameters for oneclass SVM while doing unsupervised learning?

For my task, I am doing unsupervised learning and I am trying to find the best possible value of the parameters gamma and nu in ...
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2answers
20 views

How to include percentage variables in PCA + K-means when some values are undefined because the denominator is 0?

I'm trying to do customer segmentation by using PCA to reduce dimensionality and then feeding the resulting principal components into a K-means algo to get at the final segments. Some of my variables ...
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6 views

Differentiate Semi-supervised vs Transductive Learning?

Can someone explain the difference between transductive learning and semi-supervised learning? Or is semi-supervised learning a type of transductive learning? Transductive learning is when we do not ...
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1answer
32 views

Finding Optimal Number of Clusters in Expectation Maximization Algorithm

I know that there are several methods (Elbow method, silhouette analysis etc) which can help me to find the best number of clusters. My questions are: What are the best methods to use in ...
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1answer
22 views

Unsupervised VAE model? [closed]

I would like to use VAE model in unsupervised learning to generate new feature. Most of the examples are supervised and semi-supervised learning. Where can I find for unsupervised learning or can it ...
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1answer
41 views

On feature scaling and weighting for clustering

The issue of feature scaling and weighting for cluster formation has been widely discussed in several books and papers as well as several questions (e.g. here ). To my understanting, variable range is ...
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1answer
28 views

Unsupervised clustering of sequence of events to subsequences

I have a big dataset of M sequences of [1 - N] events, where each event has multiple properties (start date, end date, location, ...
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1answer
24 views

Unsupervised methods for Detecting Spam

What are some common unsupervised methods used to detect whether something is spam or not? For example, we may be given a large corpus of emails and need to determine whether any one of them is spam. ...
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1answer
82 views

How can t-SNE or UMAP embed new (test) data, given that they are nonparametric?

I have started using the UMAP method for dimension reduction which is a similar method to t-SNE, Diffusion Maps, Laplacian Eigenmaps, etc. The named dimension reduction methods have in common that ...
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1answer
16 views

Why are Generative Adversarial Networks classed as unsupervised

The title of the question is basically all I'm asking, but I should explain why GANs don't seem to be unsupervised to me! Here's my understanding of unsupervised learning: Unsupervised learning is ...
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1answer
57 views

What is the assumption on the distribution of data in gaussian mixture models?

I am reading about Gaussian mixture models from this slide https://www.ics.uci.edu/~smyth/courses/cs274/notes/EMnotes.pdf However, I am super confused at the very first line. It says: We ...
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1answer
25 views

When is it okay to label data yourself? (And semi-supervised learning)

i'm pretty new to machine learning so i think this might be a realy basic question. Let's imagine the following scenario: I want to classify projects as either active or inactive. Projects can be ...
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2answers
655 views

Why use a Gaussian mixture model?

I am learning about Gaussian mixture models (GMM) but I am confused as to why anyone should ever use this algorithm. How is this algorithm better than other standard clustering algorithm such as $K$-...
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43 views

Shared latent spaces

I have two interrelated response variables $A$ and $B$ over each observation $i$ in my data. I am trying to create an unsupervised model where observations could be explained by means of latent spaces(...
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41 views

Training a neural net as a classifier when there are no labels

I have an ML problem where I have a large data set and in this data set there are N categories. We have no labels. I want to be able to take this data set and use it to train a neural network to ...
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1answer
33 views

clustering location based on sorted time

I clustered my dataset based on location using DBSCAN(haversine). Everything is OK until this. However, I'd like to use the time series while I'm clustering my dataset. For example. You were at home ...
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0answers
11 views

Find a set of k non-negative vectors that explain most of the variance of the dataset?

I have a set of securities and I am looking for long-only portfolios that explain most of the variance of the set of securities. If it weren't for the long-only requirement, I could have used ...
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1answer
32 views

How to predict without labeled data

I have a data set for patients visiting emergency departments containing following features: The output variable in this data set is "disposition" - whether a patient becomes admitted or discharged. ...
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0answers
37 views

Is there a well established algorithm to match two documents on a semantic level?

I have a set of documents from a wide variety of topics and I would like to retrieve the ones that are more similar to a new document provided. A search based on common words is not good enough, so ...
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2answers
29 views

Meaning of Probability Distributions in RBMs

I'm new to machine learning, and am trying to understand some of the basics of Restricted Boltzmann Machines. Unfortunately, I don't have a background in statistics yet beyond a basic understanding, ...
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1answer
44 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 ...
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1answer
96 views

Image feature extraction using an Autoencoder combined with PCA

Background: I have fairly large dataset of biomedical images (around 10,000 images) of 1920x1920 pixels (after cropping parts of black borders out). My task is to extract the 200 most important ...
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1answer
39 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-...
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0answers
29 views

How do unsupervised credit scoring models that don't consider historical financial data work?

There seems to be a number of startups (Zest Finance, Credolab etc.) that provide credit scoring schemes that rely exclusively on alternative data without considering users historical financial data ...
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1answer
38 views

Clustering circles with different radii with Gaussian Mixture Models

I am interested in clustering $N$ circles in the plane with varying radii using a Gaussian mixture model. The radius of each circle is an integer number $R_i\in\mathbb{N}$ determined from observation. ...
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0answers
11 views

How do I calculate ensemble-averaged PCA scores?

For context, please see https://arxiv.org/pdf/1808.00084.pdf (page 6) I am trying to replicate the results of the above link, which uses dimensionality reduction to observe a phase transition in an ...
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1answer
21 views

Searching for the correct approach

I'm writing my Bachelor Thesis within the field of neural networks and I need some preparation of the data I'm using. Do you have an idea how I am able to identify the four levels in this graph via ...
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2answers
105 views

Unsupervised Learning: Train Test division

I have one conceptual question. In Unsupervised Learning, when I have no labels. The anomaly detection model (Isolation forests, Autoencoders, Distance-based methods etc.), it should fit on a ...
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1answer
94 views

When does my unsupervised autoencoder start to overfit?

I am working on anomaly detection using an autoencoder neural network with $1$ hidden layer. This is an unsupervised setting, as I do not have previous examples of anomalies. The input data has ...
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0answers
29 views

Understanding short animation about Dirichlet Process Mixture Model

On the wikipedia page of Dirichlet Process, there is the following video. I don't get the point of the video. My first impression was that the video was showing the fitting of one-dimensional data ...
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2answers
48 views

Confusion in modelling finite mixture model

From the book "Machine Learning a probabilistic Perspective", I'm reading about finite/infinite mixture models. Particularly at paragraph 25.2.1 it's stated: The usual representation (of a finite ...
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1answer
100 views

Custom Loss Function - Inducing sparsity

From the comments, I realized that my question wasn't clear enough, so I'll start with a short background. I am trying to construct an attention model that performs classification based on just a ...
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0answers
33 views

Performing clustering without a distance matrix

I have n vectors and a matrix of similarity scores between them (e.g. vector 1 score of similarity with vector 4 is 1.3, and with vector 7 is 2.3). This matrix is ...
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0answers
128 views

Clustering as a method to find and label classes for supervised learning

I'm working on a text classification project. We have around 300k documents (small, 1~2 phrases) and we don't know the set of labels or how many labels there are. The recommended approach to me was ...
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1answer
43 views

Latent variable in Gaussian Mixture Model

Whenever I look up material pertaining to Gaussian Mixture Models, it always mentions latent variable $z$, where $z \in \{1, ..., K\}$ and is one-hot encoded. I completely understand the objective of ...
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2answers
157 views

Why are decision trees considered supervised learning?

It seems to work similar to clustering algorithms, where data does not have to be labeled, and the algorithm creates it's own labels/groups based on feature similarities...
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0answers
34 views

clustering objects in point cloud

I am currently working on point cloud data analysis, trying to label objects which are not ground or vegetation e.t.c. So far I tried many clustering algorithms, with moderate success. In my best ...
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0answers
18 views

DBSCAN loops one or several times a data point?

I am trying to construct a model data to facilitate the clustering algortihms execution in terms of searching for data point in the dataset. This model is a set of connections between points such that ...
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2answers
77 views

External validation of clustering requires labels, but why cluster at all if you have labels?

There are two types of validation in clustering, using: Internal indexes: Used to measure the goodness of a clustering structure without respect to external information (e.g., sum of squared errors)...
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1answer
25 views

How can I tell if an Autoencoder is encoding my data properly?

Autoencoders can be classified as a method of unsupervised learning, and unsupervised learning often comes with a problem where it's hard to tell if the model is working properly. However, some ...
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88 views

Clustering Noisy Data [closed]

There are various ways to cluster data. Some require the data first to be scaled to have a mean of $0$ and standard deviation of $1$. However, others do not mention if the data should be scaled at all....