Questions tagged [unsupervised-learning]

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

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24
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1answer
26k views

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. ...
20
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2answers
12k views

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, ...
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3answers
2k views

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 ...
13
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4answers
13k views

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 ...
12
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3answers
4k views

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) ...
11
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2answers
9k views

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 ...
11
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3answers
10k views

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 ...
10
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1answer
58k 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 ...
10
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2answers
12k views

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, ...
10
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1answer
143 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 ...
9
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1answer
4k views

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?
8
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1answer
750 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 ...
7
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3answers
20k 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 ...
7
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3answers
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. ...
7
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6answers
848 views

Clustering algorithms for high dimensional binary sparse data

I have a dataset with 10,000 genes like below ...
6
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2answers
7k 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 ...
6
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1answer
17k views

K-Means vs hierarchical clustering [closed]

When hierarchical clustering is preferred over k means clustering?
6
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2answers
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 ...
6
votes
3answers
164 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-...
6
votes
1answer
237 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 ...
5
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3answers
1k 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 ...
5
votes
2answers
4k 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 ...
5
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1answer
2k views

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 ...
5
votes
1answer
832 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 = ${...
5
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1answer
1k 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 ...
5
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1answer
577 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 ...
5
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2answers
2k 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?
5
votes
1answer
152 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 ...
5
votes
2answers
5k 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 ...
5
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3answers
222 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 ...
5
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3answers
812 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 ...
5
votes
1answer
106 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 ...
5
votes
1answer
2k views

Isolation forest sklearn contamination param

I'm working on an unsupervised anomaly detection task on time series using isolation forest algorithm. I'm developing in Python, more in detail using sklearn. I found out a lot of examples on this, ...
5
votes
4answers
443 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 ...
5
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2answers
133 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 ...
4
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2answers
628 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 ...
4
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1answer
392 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 ...
4
votes
3answers
196 views

ML algorithm for Music Features

I am a newbie in machine learning topic and I need to create model from music data. It contains features of the songs but it is not labeled. How can I create a model from that ? Do I need to use ...
4
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1answer
69 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: ...
4
votes
1answer
171 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 ...
4
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2answers
2k 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 ...
4
votes
2answers
89 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 "...
4
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1answer
3k views

How to find patterns in a series of timestamps

I have a series of timestamps that represent the time a user clicked a certain button. My goal is to detect the automated clicks, so I need to find recurring patterns in the data that may point to an ...
4
votes
3answers
576 views

Using an unsupervised Isolation Forest, how does one identify the optimal number of outliers from the anomaly scores?

I am using an unsupervised isolation forest algorithm and computing anomaly scores to detect outliers from a 2 dimensional toy dataset. From a scatter plot, I am able to detect/visualize the data ...
4
votes
1answer
278 views

How to use a different model to deep neural network with reinforcement learning based on DQN?

Is it possible to implement a reinforcement learning algorithm without using a deep neural network (DNN) as used in deep reinforcement learning e.g. Deep Q-Network (DQN)? How can I replace the DNN in ...
4
votes
1answer
423 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 ...
4
votes
1answer
27 views

semi supervised learning doubt only classify points with confidence above threshold

I currently have a dataset with approximately 5% labelled points and 95% unlabelled. I would like to label some of the unlabelled points only if I am very confident and leave the rest NaN. Personally ...
4
votes
1answer
36 views

Is there a paper accomplishing finding physical law from observation without premade perception, using machine learning?

For example: Isaac Newton finds law of universal gravitation just by looking a falling apple, without any premade perception of that phenomenon. Is it possible to accomplish that kind of discovery ...
4
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1answer
161 views

Probability for label correctness in semi-supervised learning

I am aware of the existence of semi-supervised learning approaches, such as the Ladder Network, where only a subset of the data is labeled. Are there any methods or papers which consider correctness ...
4
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5answers
5k views

Best approach for this unsupervised clustering problem with categorical data?

I'm a software engineer new to Machine Learning. I've read about basic non-supervised techniques like k-means and hierarchical clustering and now I'm trying to put them into practice with a basic ...

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