Questions tagged [unsupervised-learning]

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

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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 ...
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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), \...
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Clustering by unsupervised RandomForest proximities, choosing parameters

Dataset: Clinical healthcare data from registry, N = 13'000, variables = 27 with different class types (numeric, categorical), outcome = survival (~20%) or death (~80%). Goal: Explanation. Identifying ...
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Required Properties of Labels for Disentangled Representation Learning

I came across this awesome paper "Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations" (also described in this nice blog post), where the authors show ...
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ML classifier comparison questions

I got some review comments as below from the conference panel regarding my results They are comparing unsupervised learning (KNN, Linear Regression) to supervised learning (CNN, RF). How are they ...
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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 ...
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Unsupervised learning (clustering) before supervised learning [closed]

Is it a common practice to do clustering before supervised learning to eliminate "noisy data"? Obviously, depending on the type of task. It seems like it makes sense in my case and my neural ...
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Alternative to PCA that maintains densities/distances

The question is the same as posed in the title; is there an alternative to PCA that doesn't rely on the linear assumption but maintains distances (i.e. the main issue with UMAP/tSNE)? Thanks!
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2 answers
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What is the relationship between noise reduction and dimension reduction?

My understanding is that unsupervised methods like PCA, autoencoders and K-means shape a data space such that the modified representation of the data either nicely separates different families of data ...
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How to evaluate the performance of recommender systems without having labeled data

I have a huge citation graph of research papers and datasets. So, there is an edge among two items when one of them cites another. So far I've used Node2Vec for creating a dataset recommender system ...
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input data for training USAD network [closed]

During reading 'USAD: UnSupervised Anomaly Detection on Multivariate Time Series' paper, to train the autoencoder the authors only used normal datasets. But since this algorithm is used for ...
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Why separating the data in training and test sets is not feasible in unsupervised learning problem?

Based on my understanding: Unsupervised learning problems are modeling data with no labels. Hence, we try to cluster a given data into clusters. Supervised learning problems are modeling data with ...
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How to interpret unsupervised results

I have applied some unsupervised techniques on text data in order to do topic modeling such as LDA. Once training is completed LDA produces associations between documents and topics. Now my question ...
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Labeling of Unlabeled data

I have some labeled data and i another unlabeled dataset how to find top 5 label from labeled data for each data of unlabeled data.Let's says 5 clostest one. I Have claculated the LSI of unlabeled ...
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1 answer
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How to determine agreement between clustering methods?

Let's say you want to compare the outcome of KMeans and KMedoids. How to determine if cluster 1 from KMeans can be compared with cluster 1 with KMedoids. Or, in other words, let's say KMeans labels ...
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K-Prototypes did not form clusters

I implemented a K-Prototypes algorithm (Huang) to cluster some mixed data in order to solve a customer segmentation question. There aren't a crazy amount of observations (n = ~6k) and with 8 fields (2 ...
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KNN(k-nearest neighbor) algorithm as supervised and as unsupervised algorithm. What are the main differences? How can it be both?

On internet and in articles KNN ist mostly described as supervised algorithm. But recently I have find also few articles where it is mentioned as unsupervised algorithm.I cannot find articles that are ...
2 votes
1 answer
150 views

Unsupervised classification of objects based on relationships

I have size measurements of 1000 objects, measured over time. I would like to classify the objects based on the response of their size to time using unsupervised classification. For example, the size ...
2 votes
1 answer
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Infer limits of unscaled values from their standardized values - Clustering

I am working on a clustering problem and I have some skewed variables. So, I log transform them and use them in clustering. However, instead of multivariate clustering, I do multiple univariate ...
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2 votes
1 answer
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Converting unsupervised to supervised problem - Overfitting - bad?

I am working on a customer segmentation using 5 features such as recency, frequency, monetary, tenure, unique_product_cnt etc. So, I did a RFM based segmentation where I used ...
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1 vote
1 answer
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standardization/normalization for 1D clustering?

I have two input variables revenue and age. Am trying to find different bins within that variables. For ex: I have ...
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1 vote
1 answer
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silhouette score vs Distortion score

I am working on segmenting my customers with clustering. My dataset size is 7315 rows and 30 features. So, as a beginner to clustering, I passed all my 29 features (excluding id column) to the cluster....
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2 votes
1 answer
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Meaningful to retrieve original value after standardization using clustering

I already referred these posts here and here. Currently, I am working on customer segmentation using their purchase data. So, my data has below info for each customer Based on the above linked posts ...
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Classifying words/phrases that say the same thing in slightly different ways

I have survey data in which participants can manually write one or more skills that they possess. Many responses are similar enough to be classified as the same but are either not identical (e.g., &...
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1 answer
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RFM Customer segmentation - Why Avg monetary value instead of total monetary value?

I am trying to segment our customers based on their purchase data. And I came to know about the RFM technique (Recency, Frequency and Monetary) through these posts here, here etc. Recency - How ...
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2 votes
1 answer
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Image Clustering (Unsupervised learning) on unknow class(guess less than 300)

I have 30000 unlabeled images (each image has only one character), and the content of the images is very simple, basically black lines(a language but not English) and white background. I hope to use ...
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Chicken and egg problem in machine learning [closed]

Recently, I went through an ICLR paper SELF-LABELLING VIA SIMULTANEOUS CLUSTERING AND REPRESENTATION LEARNING. In the paper, authors discussed simultaneously labeling the images and training a network ...
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Any reason for choosing t-SNE over UMAP when visualizing?

According to the UMAP paper: Our algorithm is competitive with t-SNE for visualization quality and arguably preserves more of the global structure with superior run time performance. paper It seems ...
2 votes
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Restricted Boltzmann Machine: W matrix visualization results after training MNIST images and Pseudo-log-likelihood

I am implementing RBM from scratch using Tensorflow and after training my RBM on the MNIST dataset for 200 epochs using Persistent CD with two steps of contrastive divergence, I learn the weights W ...
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Do we need to split the data for Unsupervised Anomaly Detection?

I'm struggling with understanding the concept of splitting data for unsupervised anomaly/outlier detection. You can find all approaches here. I found some papers and implementations that didn't split ...
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Maximizing a unique trace quadratic form

I am dealing with an unsupervised problem where I have ended up with the following maximization problem: $\max_{C\in \mathbb{R}^{p\times n}}\sum_{i=0}^{m} tr(CA^ixx^\top A^{i^\top}C^\top) \\ \mathrm{...
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In which category falls a mix of unsupervised and supvervised learning?

Here is the context of my problem: I want to classify between to classes. However, I have at disposal only non labeled data to do the training (the test set possess all labels for evaluation purposes)....
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Why does single linkage create loose clusters when it uses smallest distance between two points?

The definition of single linkage says: In single linkage method, the distance between two clusters is defined as the minimum distance between two data points in each cluster. However, different ...
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1 answer
186 views

Can all neural network layers be used as either a supervised or an unsupervised model?

I am trying to understand neural networks and by reading different articles I always find conflicting information. I wanted to understand which neural networks can be used as supervised/unsupervised. ...
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Testing if one sample can be from a specific class

Suppose I have 2 customer segments in a bank (private class and another class) defined by the institution itself. and I want to increase the amount of private clients class, bring clients from other ...
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1 answer
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A method for clustering 1D signals?

I have samples from 150 different genes containing the following information: sequence of the gene signal strength along the length of the gene (the signal can be negative or positive). I have ...
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analytically determine how many clusters you need to get an explained variance of over x%

I am currently trying to cluster my data with as few clusters as possible. I have tried using K-means clustering and spectral clustering. Both work relatively well, around 85% explained variance from ...
2 votes
1 answer
24 views

Are Hidden Markov Models the right tool for signal segmentation task?

I have a particular problem, and I would like to know if using a HMM is the correct tool for it. Apologies for the poor wording of the problem, HMMs are definitely not my specialty. I have the ...
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Are the distances on a hierarchical clustering dendrogram in the same units as the input distance matrix?

I use Aitchison distance as the input to a hierarchical clustering dendrogram. I started labeling and interpreting the dendrogram but wasn't sure about a few aspects: Are the vertical distances on ...
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Embedding extraction -> Classifier VS Embedding learning+ Classification on-the-fly?

I have two questions: How should we compare in general which of the following perform better? I have a graph and would like to perform a graph classification task. Is it better to extract graph ...
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What is the proper way to externally validate clusters when I have only a sample of the dataset labeled, but want to cluster the entire dataset?

I have a dataset of text-based documents that I want to cluster. For a sample of this dataset (~10%) I have manually annotated labels (i.e., the ground truth). I would like to cluster this dataset to &...
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mixture of finite regressions without a response variable

In finite mixture modelling, in particular mixture of regressions modelling, we are interested in finding latent trajectories against a response variable. But what if there is no known response ...
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is there neural network architecture for unsupervised learning for topic modelling?

Let say i have a bunch of document and I want to analyse the topic of the corpus. The only way i know is to use unsupervised learning with gensim which use model like Latent Dirichlet allocation(LDA),...
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What is the best approach: Labeled training data and unlabeled test data [closed]

I'm new into the data science world and I am working on improving my knowledge so here is my problem: I want to build a binary classifier with the following constraints: I have 2 files training.csv ...
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PCA explained variance and model inertia

I'm trying to perform a PCA to reduce the dimensionality of my data and subsequently perform a K-Means algorithm. I initially chose 4 Principal Components because they explain 70% of my variance. This,...
1 vote
1 answer
33 views

Why are my classification outputs sometimes saturating? [closed]

I have built a binary classifier (FWIW - using Keras) but the output values saturate at a value considerably < 1 (see plot). Is this likely to be implementation problem? Is it because the model is ...
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Can a Supervised Routine be Compared Against an Unsupervised?

Just a question out of curiosity. Suppose that I had generated: (1) an unsupervised decision tree using 'interpretable clustering,' and (2) a second supervised decision tree (whether CART, or a ...
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1 vote
1 answer
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why is unsupervised learning said to be learning probability distribution?

In the book Deep Learning by Ian Goodfellow et al., it is mentioned that unsupervised learning involves observing several examples of a random vector x, and attempting to implicitly or explicitly ...
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Correlation among categorical variable and unsupervised model with practical example

I'd like to demonstrate that men will apply for a job if their skills match at least with the 60% of the requirements in job advertisement; women only if they match 100%. I'll use R. I'll give you a ...
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How to save a Higher accurate K-means Model on a unlabelled data based on Any Performance Evaluation Metrics?

I am experimenting on Iris dataset. I am not using the label. I want to save my model based on any Performance Metrics. According to Performance Metrics which model have higher score I am choosing ...
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