Questions tagged [unsupervised-learning]

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

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Word2Vec vs. Sentence2Vec vs. Doc2Vec

I recently came across the terms Word2Vec, Sentence2Vec and Doc2Vec and kind of confused as I am new to vector semantics. Can someone please elaborate the differences in these methods in simple words. ...
Smith's user avatar
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25 votes
2 answers
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What kinds of learning problems are suitable for Support Vector Machines?

What are the hallmarks or properties that indicate that a certain learning problem can be tackled using support vector machines? In other words, what is it that, when you see a learning problem, makes ...
Ragnar's user avatar
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16 votes
3 answers
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Intuition Behind Restricted Boltzmann Machine (RBM)

I went through Geoff Hinton's Neural Networks course on Coursera and also through introduction to restricted boltzmann machines, still I didn't understand the intuition behind RBMs. Why do we need ...
Born2Code's user avatar
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14 votes
4 answers
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How word2vec can be used to identify unseen words and relate them to already trained data

I was working on word2vec gensim model and found it really interesting. I am intersted in finding how a unknown/unseen word when checked with the model will be able to get similar terms from the ...
gaurus's user avatar
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13 votes
3 answers
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How can autoencoders be used for clustering?

Suppose I have a set of time-domain signals with absolutely no labels. I want to cluster them in 2 or 3 classes. Autoencoders are unsupervised networks that learn to compress the inputs. So given an ...
Tendero's user avatar
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13 votes
3 answers
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How to use GAN for unsupervised feature extraction from images?

I have understood how GAN works while two networks (generative and discriminative) compete with each other. I have built a DCGAN (GAN with convolutional discriminator and de-convolutional generator) ...
exAres's user avatar
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12 votes
2 answers
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Does it make sense to train a CNN as an autoencoder?

I work with analyzing EEG data, which will eventually need to be classified. However, obtaining labels for the recordings is somewhat expensive, which has led me to consider unsupervised approaches, ...
Kaare's user avatar
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12 votes
2 answers
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Clustering high dimensional data

TL;DR: Given a big image dataset (around 36 GiB of raw pixels) of unlabeled data, how can I cluster the images (based on the pixel values) without knowing the number of clusters ...
sunside's user avatar
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12 votes
1 answer
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What is the difference between topic modeling and clustering?

I know that topic modeling and clustering are related, but not similar techniques. Can anyone suggest what are the main differences?
sara's user avatar
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10 votes
1 answer
64k views

Confused about how to apply KMeans on my a dataset with features extracted

I am trying to apply a basic use of the scikitlearn KMeans Clustering package, to create different clusters that I could use to identify a certain activity. For example, in my dataset below, I have ...
Gary's user avatar
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10 votes
1 answer
181 views

Robustness of ML Model in question

While trying to emulate a ML model similar to the one described in this paper, I seemed to eventually get good clustering results on some sample data after a bit of tweaking. By "good" results, I mean ...
Alerra's user avatar
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9 votes
3 answers
10k views

Isolation forest sklearn contamination param

I am working on an unsupervised anomaly detection task on time series data using an isolation forest algorithm. I am developing it in Python, more in detail using ...
Giordano's user avatar
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8 votes
1 answer
810 views

Ideas for prospect scoring model

I have to think about a model to identify prospects (companies) that have a high chance of being converted into clients, and I'm looking for advice on what kind of model could be of use. The ...
François M.'s user avatar
7 votes
4 answers
25k views

is it possible to do feature selection for unsupervised machine learning problems?

I started looking for ways to do feature selection in machine learning. By having a quick look at this post , I made the assumption that feature selection is only manageable for supervised learning ...
ChiPlusPlus's user avatar
7 votes
2 answers
11k views

Why will the accuracy of a highly unbalanced dataset reduce after oversampling?

I have created a synthetic dataset, with 20 samples in one class and 100 in the other, thus creating an imbalanced dataset. Now the accuracy of classification of the data before balancing is 80% while ...
girl101's user avatar
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7 votes
1 answer
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Gaussian Mixture Models as a classifier?

I'm learning the GMM clustering algorithm. I don't understand how it can used as a classifier. Here are my thought: 1) GMM is an unsupervised ML algorithm. At least that's how ...
F.S.'s user avatar
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7 votes
4 answers
26k views

How to do feature selection for clustering and implement it in python?

I am trying to implement k-means clustering on 60-70 features and I came across a post for feature selection technique on quora by Julian Ramos, but I fail to understand few steps mentioned. I am ...
Akash Dubey's user avatar
7 votes
3 answers
23k views

Anomaly detection on time series

I've just started working on an anomaly detection development in Python. My data sets are a collection of timeseries. More in details, data are coming from some sensors/meters which record and ...
Giordano's user avatar
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7 votes
3 answers
1k views

Which outlier detection can detect these outliers?

I have a vector and want to detect outliers in it. The following figure shows the distribution of the vector. Red points are outliers. Blue points are normal points. Yellow points are also normal. ...
Arkan's user avatar
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7 votes
6 answers
1k views

Clustering algorithms for high dimensional binary sparse data

I have a dataset with 10,000 genes like below ...
asdlfkjlkj's user avatar
7 votes
4 answers
1k views

Cluster evolution over time

I have a dataset of transactional data with customer ID and I want to segment the dataset into groups using cluster analysis. I'm interested in following the evolution of each cluster over time, but ...
Egodym's user avatar
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6 votes
1 answer
20k views

K-Means vs hierarchical clustering [closed]

When hierarchical clustering is preferred over k means clustering?
Michael Laster's user avatar
6 votes
1 answer
3k views

What does it mean by “t-SNE retains the structure of the data”?

I was learning about t-SNE when I was told that t-SNE retains the structure of the data in the embeddings. What exactly does this mean ? How does the algorithm achieve this ? So far I have ...
Tanmay Bhatnagar's user avatar
6 votes
2 answers
5k views

What is the meaning of spherical dataset?

In the following article, one of the statement is as follows: The K-means algorithm is effective only for spherical datasets What does spherical dataset mean?
girl101's user avatar
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6 votes
2 answers
4k views

What methods can be used to detect anomalies in temporal texual data?

I've been looking for methods that can help figure out anomalies in textual data stored in databases. Major goal is to use a unsupervised learning method to detect the anomalies. Further how can I ...
Sanjeev Rathor's user avatar
6 votes
3 answers
447 views

Why spectral clustering results in disjointed cluster?

I'm working on a project where I have to dynamically cluster the position of objects with respect to one coordinate. So I'm essentially dealing with subsequent frames and each frame represents a one-...
Kuba_'s user avatar
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6 votes
1 answer
447 views

How to compare two unsupervised anomaly detection algorithms on the same data-set?

I want to solve an anomaly detection problem on an unlabeled data-set. The only information about this problem is that the anomalies population is lower than 0.1%. It should be notice that the size of ...
Alireza Zolanvari's user avatar
5 votes
3 answers
2k views

To detect unauthorized access using outlier detection

I am working on project where my task is to find unauthorized access using any machine learning technique. Let me clear my problem definition. UserA access website using chrome browser from windows ...
Nilesh Shaikh's user avatar
5 votes
2 answers
7k views

Is Overfitting a problem in Unsupervised learning?

I come to this question as I read the use of PCA to reduce overfitting is a bad practice. That is because PCA does not consider labels/output classes and so Regularization is always preferred. That ...
Ronak Agrawal's user avatar
5 votes
1 answer
1k views

Combine two sets of clusters

I have two sets of topics obtained from two different sets of news paper articles. In other words, Cluster_1 = ${x_1, x_2, ..., x_n}$ includes the main topics of 'X' news paper set and Cluster_2 = ${...
Volka's user avatar
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5 votes
2 answers
8k views

supervised learning and labels

In this wiki page, I came across with the following phrase. When data is not labeled, a supervised learning is not possible, and an unsupervised learning is required I cannot figure out why ...
tharindu_DG's user avatar
5 votes
1 answer
489 views

What is major difference between different dimensionality reduction algorithms? [closed]

I find many algorithms are used for dimensionality reduction. The more commonly used ones (e.g. on this page ) are: ...
rnso's user avatar
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5 votes
1 answer
677 views

Unsupervised feature reduction for anomaly detection with autoencoders

I am collecting a big number of generated numeric features for the task of unsupervised anomaly detection. I can assume that all training data is considered normal. I expect some of the generated ...
Yuval's user avatar
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5 votes
1 answer
171 views

Inferring Relational Hierarchies of Words

I am new to natural language processing and I have not heard of a problem similar to mine yet. I was wondering if anyone could refer me to a method for solving my problem, or tell me how this problem ...
Pholochtairze's user avatar
5 votes
3 answers
359 views

Looking for a classification (?) algorithm for linearly separable but unlabeled data points

I have a dataset that is linearly separable with two lines - something like that: Now I'am looking for the right kind of algorithm to do what I guess a SVM would do with labeled data - find the ...
Benj's user avatar
  • 126
5 votes
3 answers
969 views

Classification technique for unsupervised data?

I have unsupervised data (i.e this data doesn't have any target variable through which I can learn it's prior behaviour) it is a mix of continuous and categorical data. Now I want to classify the test ...
Rahul Sharma's user avatar
5 votes
1 answer
347 views

How do I interpret my result of clustering?

I am working on a clustering problem. I have 11 features. My complete data frame has 70-80% zeros. The data had outliers that I capped at 0.5 and 0.95 percentile. However, I tried k-means (python) on ...
Akash Dubey's user avatar
5 votes
1 answer
3k views

Estimating Predictive Uncertainty for unlabeled data

I am trying to estimate the predictive uncertainty for a deep neural network. While I do have a labeled training set, I´m trying to measure uncertainty for some unlabeled production data. This paper ...
L. Anders's user avatar
5 votes
1 answer
754 views

Clustering for high dimensional data

I am have a data set with 52 variables. Most of them have zeros, it resembles a sparse matrix. How can I cluster this kind of data and are there any special types of clustering? I am attaching pca ...
dileep balineni's user avatar
5 votes
2 answers
3k views

Constrained k-means algorithms in R (must-link constraints)

I currently face an unsupervised learning task that is to be approaches using clustering. More specifically, it is a segementation task and hence there is some prior knowledge about a) the number of ...
user4985694's user avatar
5 votes
4 answers
497 views

Categorical Clustering of Users Reading Habits

I have a data set with a set of users and a history of documents they have read, all the documents have metadata attributes (think topic, country, author) associated with them. I want to cluster the ...
Jonathan Dine's user avatar
5 votes
0 answers
2k views

Dealing with categorical variables in Isolation Forest

Isolation Forest is widely used when dealing with outlier/anomaly detection when we have no labels. The theory behind is that making random split at random points and counting how many splits you do ...
Carlos Mougan's user avatar
4 votes
2 answers
1k views

Is SVD non-linear while PCA (by eigendecompostion) is linear?

I am quite confused because a colleague of mine recently told me that he preferred using SVD instead of PCA (by eigendecomposition) because, contrary to the latter, the former is non-linear so it can ...
Outcast's user avatar
  • 1,037
4 votes
1 answer
721 views

Can a novelty detection model overfit?

Can a novelty detection model overfit? In novelty detection, the model is trained on normal data instances (not polluted by outliers) where no labels are used in the training process, while validated ...
s_am's user avatar
  • 53
4 votes
4 answers
1k views

What machine learning algorithms to use for unsupervised POS tagging?

I am interested in an unsupervised approach to training a POS-tagger. Labeling is very difficult and I would like to test a tagger for my specific domain (chats) where users typically write in lower ...
Tido's user avatar
  • 193
4 votes
3 answers
5k views

What approach other than Tf-Idf could I use for text-clustering using K-Means?

I am working on a text-clustering problem. My goal is to create clusters with similar context, similar talk. I have around 40 million posts from social media. To start with I have written clustering ...
Suhail Gupta's user avatar
4 votes
1 answer
177 views

Research in random forest algorithms able to switch data sets

I'm curious as to whether research been done into random forests that combine unsupervised with supervised learning in a way allowing a single algorithm to find patterns in, and work with, multiple ...
Jessiah Burgess's user avatar
4 votes
3 answers
182 views

Which ML approach is the best for huge state spaces?

My issue derives from the challenge of solving a seemingly easy-looking game. To spare you the full catalogue of rules, here is a short summary of the game: Single player card game You go through a ...
J. M. Arnold's user avatar
4 votes
3 answers
3k views

Clustering vs Non Clustering problems?

I'm just getting started with Andrew Ng's Machine Learning wherein he explained the example of the cocktail party problem vs the gene clustering problem in order to explain the difference between ...
Hrishikesh Athalye's user avatar
4 votes
2 answers
126 views

When using an unsupervised alogirthm, what is the "learning" part since it belong to machine learning field?

I had a brief experience with machine learning by using a clustering algorithm, i also read the basic ideas and calculations of a simple classification algorithm. Now, i would read more about "...
zamine's user avatar
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