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Questions tagged [unsupervised-learning]

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

28
votes
2answers
38k views

Choosing the right linkage method for hierarchical clustering

I am performing hierarchical clustering on data I've gathered and processed from the reddit data dump on Google BigQuery. My process is the following: Get the latest 1000 posts in /r/politics ...
26
votes
4answers
26k views

Evaluation measures of goodness or validity of clustering (without having truth labels)

I'm clustering a set of data but I don't have truth document that allow me to evaluate the result of clustering (I have unlabelled data), so I can not use an external evaluation measure. In this case, ...
37
votes
1answer
52k views

Performance metrics to evaluate unsupervised learning

With respect to the unsupervised learning (like clustering), are there any metrics to evaluate performance?
19
votes
1answer
17k views

How to define number of clusters in K-means clustering?

Is there any way to determine the optimal cluster number or should I just try different values and check the error rates to decide on the best value?
21
votes
5answers
11k views

Clustering procedure where each cluster has an equal number of points?

I have some points $X=\{x_1,...,x_n\}$ in $R^p$, and I want to cluster the points so that: Each cluster contains an equal number of elements of $X$. (Assume that the number of clusters divides $n$.) ...
76
votes
3answers
148k views

How to produce a pretty plot of the results of k-means cluster analysis?

I'm using R to do K-means clustering. I'm using 14 variables to run K-means What is a pretty way to plot the results of K-means? Are there any existing implementations? Does having 14 variables ...
26
votes
3answers
24k views

Unsupervised, supervised and semi-supervised learning

In the context of machine learning, what is the difference between unsupervised learning supervised learning and semi-supervised learning? And what are some of the main algorithmic approaches to ...
14
votes
3answers
8k views

How to choose an optimal number of latent factors in non-negative matrix factorization?

Given a matrix $\mathbf V^{m \times n}$, Non-negative Matrix Factorization (NMF) finds two non-negative matrices $\mathbf W^{m \times k}$ and $\mathbf H^{k \times n}$ (i.e. with all elements $\ge 0$) ...
13
votes
4answers
20k views

Initializing K-means centers by the means of random subsamples of the dataset?

If I have a certain dataset, how smart would it be to initialize cluster centers using means of random samples of that dataset? For example, suppose I want ...
29
votes
5answers
4k views

Distinguishing between two groups in statistics and machine learning: hypothesis test vs. classification vs. clustering

Assume I have two data groups, labeled A and B (each containing e.g. 200 samples and 1 feature), and I want to know if they are different. I could: a) perform a statistical test (e.g. t-test) to see ...
21
votes
2answers
12k views

Generative vs discriminative models (in Bayesian context)

What are the differences between generative and discriminative (discriminant) models (in the context of Bayesian learning and inference)? and what it is concerned with prediction, decision theory or ...
19
votes
2answers
6k views

What is the manifold assumption in semi-supervised learning?

I am trying to figure out what the manifold assumption means in semi-supervised learning. Can anyone explain in a simple way? I cannot get the intuition behind it. It says that your data lie on a low-...
9
votes
2answers
10k views

Feature selection for clustering problems

I am trying to make group together different datasets using unsupervised algorithms (clustering). The problem is that I have many features (~500) and a small amount of cases (200-300). So far I used ...
8
votes
1answer
2k views

Self organizing maps vs. kernel k-means

For an application, I want to cluster data (potentially high dimensional) and extract probability of belonging to a cluster. I consider at the moment Self organizing maps or kernel k-means to do the ...
14
votes
3answers
482 views

What *is* an Artificial Neural Network?

As we delve into Neural Networks literature, we get to identify other methods with neuromorphic topologies ("Neural-Network"-like architectures). And I'm not talking about the Universal Approximation ...
6
votes
2answers
2k views

Hidden Markov Models with multiple emissions per state

I want to use Hidden Markov Models for an unsupervised sequence tagging problem. Due to the peculiarities of my application domain (recognition of dialogue acts in conversations), I would like to use ...
5
votes
1answer
3k views

What is the minimum number of data points required for kernel density estimation?

What is the minimum number of data points required for a kernel density estimation to be considered non-misleading/acceptable/adequate? Is there a some rule based on how dispersed the data is? For ...
3
votes
1answer
321 views

How to consider different samples in functional data clustering?

In the engineering context several data sources like different kinds of measurement signals (for example distances, angles and efficiencies) are very common. If it would be possible to observe these ...
5
votes
1answer
240 views

What is mean by the non-gaussianity in the independent component analysis(ICA)?

What is mean by non-gaussianity in ICA? Why do we use in ICA? How is Non-Gaussianity is an important and essential principle in ICA estimation? Following is the statement I found in a research paper....
1
vote
1answer
1k views

How to deal with a variable-sized real vector of inputs?

I have a collection of objects with properties that I measure. For each object, I obtain a vector of real numbers describing that object. Each object results in a vector having a different length. I ...
1
vote
1answer
215 views

Why does Latent Dirichlet Allocation seems to work with greedy selection but not with Gibbs sampling?

I tried to implement my own LDA program in python, while following this tutorial. When I use gibbs sampling, the program assigns all words to a particular topic on convergence. When I greedily ...
48
votes
2answers
77k views

How can an artificial neural network ANN, be used for unsupervised clustering?

I understand how an artificial neural network (ANN), can be trained in a supervised manner using backpropogation to improve the fitting by decreasing the error in ...
29
votes
2answers
32k views

Supervised learning, unsupervised learning and reinforcement learning: Workflow basics

Supervised learning 1) A human builds a classifier based on input and output data 2) That classifier is trained with a training set of data 3) That classifier is tested with a test set of data 4) ...
31
votes
4answers
28k views

What are the differences between sparse coding and autoencoder?

Sparse coding is defined as learning an over-complete set of basis vectors to represent input vectors (<-- why do we want this) . What are the differences between sparse coding and autoencoder? ...
12
votes
4answers
5k views

Can you compare different clustering methods on a dataset with no ground truth by cross-validation?

Currently, I am trying to analyze a text document dataset that has no ground truth. I was told that you can use k-fold cross validation to compare different clustering methods. However, the examples I ...
22
votes
3answers
36k views

Supervised clustering or classification?

The second question is that I found in a discussion somewhere on the web talking about "supervised clustering", as far as I know, clustering is unsupervised, so what is exactly the meaning behind "...
13
votes
3answers
3k views

Choosing the hyperparameters using T-SNE for classification

In as specific problem that I work with (a competition) I have the follwoing setting: 21 features (numerical on [0,1]) and a binary output. I have approx 100 K rows. The setting seems to be very noisy....
21
votes
3answers
3k views

How to predict outcome with only positive cases as training?

For the sake of simplicity, let's say I'm working on the classic example of spam/not-spam emails. I have a set of 20000 emails. Of these, I know that 2000 are spam but I don't have any example of not-...
8
votes
2answers
2k views

Why only the mean value is used in (K-means) clustering method?

In clustering methods such as K-means, the euclidean distance is the metric to use. As a result, we only calculate the mean values within each cluster. And then adjustments are made on the elements ...
17
votes
1answer
12k views

Distant supervision: supervised, semi-supervised, or both?

"Distant supervision" is a learning scheme in which a classifier is learned given a weakly labeled training set (training data is labeled automatically based on heuristics / rules). I think that both ...
8
votes
2answers
2k views

Cluster clickstream data

I've recently entered the realm of machine learning and a project I am working on requires me to cluster users based on the order they visited webpages on a website. I have data in the form of: ...
7
votes
2answers
1k views

Comparing 2 classifiers with unlimited training data

I would like to compare 2 text classifiers C1 and C2, which can be trained with "unlimited" noisy training datasets, meaning that you can use as much data as you want for training, such data being ...
4
votes
1answer
2k views

How can I assess how descriptive feature vectors are?

I am assessing how good different features are for unsupervised classification of a set of objects. For each different feature I test, I have computed a feature vector that describes the object. I ...
7
votes
1answer
3k views

When to use LDA over GMM for clustering?

I have a dataset containing user activity with 168 dimensions, where I want to extract clusters using unsupervised learning. It is not obvious to me whether to use a topic modelling approach in Latent ...
1
vote
1answer
2k views

How to train and fine-tune fully unsupervised deep neural networks?

In scenario 1, I had a multi-layer sparse autoencoder that tries to reproduce my input, so all my layers are trained together with random-initiated weights. Without a supervised layer, on my data this ...
2
votes
1answer
359 views

How can unsupervised learning be performed by connectionist (neural network) algorithms?

Connectionist models of the mind (a subclass of which are neural networks) can be used to model a number of different behaviors, including language acquisition. They consist of a number of different ...
2
votes
2answers
1k views

Can you use discriminant analysis to classify new observations into categories generated by a previous $k$-means clustering?

After doing k-means clustering on a set of observations, I would like to construct a discriminant function so as to classify new observations into the categories I found after k-means. Is this at all ...
1
vote
2answers
36 views

Define attribute importance in unsupervised learning [closed]

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 ...