Questions tagged [unsupervised-learning]

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

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20
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1answer
20k 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. ...
16
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2answers
7k 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 ...
11
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4answers
11k 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 ...
9
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2answers
6k 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 ...
9
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2answers
10k 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, ...
9
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3answers
3k 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) ...
8
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1answer
50k 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 ...
8
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1answer
714 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 ...
8
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3answers
824 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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2answers
8k 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 ...
7
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1answer
3k 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?
7
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6answers
612 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
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
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1answer
186 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 ...
6
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3answers
103 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-...
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
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1answer
13k views

K-Means vs hierarchical clustering [closed]

What use cases does it make more sense to use hierarchical clustering as opposed to K-Means and vice versa?
5
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1answer
693 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
524 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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1answer
135 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
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2answers
3k 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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1answer
846 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
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3answers
167 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
768 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
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1answer
43 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
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3answers
15k 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 ...
5
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1answer
825 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
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4answers
391 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 ...
4
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2answers
5k 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 ...
4
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2answers
437 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
577 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 = ${...
4
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2answers
3k 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 ...
4
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2answers
1k 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?
4
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1answer
167 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
75 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 "machine ...
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
296 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
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1answer
337 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
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1answer
23 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
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5answers
3k 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 ...
4
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2answers
2k 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 ...
3
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2answers
151 views

Suggestion for stacked modelling in machine learning

I have built several models on the training dataset and i am not happy with the results and I wish to club them all together and generate a new model, so here is my idea as i already have the results ...
3
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3answers
143 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 ...
3
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2answers
203 views

which NN should I use for Time-series dataset, whose pattern change as time goes

I am analyzing a time-series dataset using (supervised) tensorflow deep learning. The tensorflow code is given a series of inputs, and based on each input, the NN has to predict output value in near ...
3
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2answers
2k 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 ...
3
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1answer
26 views

Is pattern recognition the same as unsupervised learning? Is machine learning the same as supervised learning?

Firstly, here is the definition of a well-posed learning problem: A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its ...
3
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1answer
432 views

ML Models: How to handle categorical feature with over 1000 unique values

I am trying to build an ML Classification model on a data set that contains quite a few categorical columns. However, few of them have over 1000 unique values. I am concerned that if I run one-hot ...
3
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1answer
2k views

Good books on unsupervised learning [closed]

I am looking for a good book about unsupervised learning that goes beyond the typical k-means and hierarchical clustering algorithms. Practical implementations in R or Python will be a plus. ...
3
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2answers
2k views

generalized likelihood ratio test (GLRT)

I am having trouble in understanding the generalized likelihood ratio test (GLRT). Can anyone explain what it is to me, or point me toward an easy-to-understand reference? Is it a supervised or ...

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