Questions tagged [unsupervised-learning]

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

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5
votes
2answers
215 views

How to apply unsupervised classification to spatial data

I am trying to learn how to apply unsupervised classification to spatial data. In near infra red satelite pictures; the ocean is dark and the forest is white. Each pixel in such an image is an ...
1
vote
2answers
218 views

SOM automated/objective clustering

So as I understand it SOM is primarily a visualization tool and clustering is a logical next step after you construct a SOM from data. Typically, the clustering is subjective in that after looking at ...
11
votes
5answers
2k views

SOM clustering for nominal/circular variables

Just wondering if anyone is familiar with clustering nominal inputs. I've been looking at SOM as a solution but apparently it only works with numerical features. Are there any extensions for ...
6
votes
2answers
1k views

How to differentiate two subgroups from a histogram?

I have a set of samples in which I assume there are 2 definite subsets in it. I plotted their values in a histogram and found that there are two distinct modes as shown in the figure below. My ...
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 ...
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?
22
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 $n$.) ...
3
votes
0answers
307 views

References on semi-supervised LDA

I'd like to perform semi-supervised LDA (Latent Dirichlet Allocation) in the following sense: I have several topics that I'd like to use, and have seed documents that relate to these topics. I'd like ...
7
votes
0answers
578 views

Recommended method for finding archetypes or clusters

I wish to cluster users together in a database, with each user represented by a number of features that are both discrete and continuous in nature. The aim is to define a small number of archetypal "...
21
votes
2answers
13k 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 ...
2
votes
2answers
4k views

Soft and Hard EM (Expectation Maximization)

What is the difference between soft and hard expectation maximization? EDIT: ok, i've found out this paper: http://ttic.uchicago.edu/~dmcallester/ttic101-07/lectures/em/em.pdf that explain quite well ...
10
votes
2answers
2k views

How to understand a convolutional deep belief network for audio classification?

In "Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations" by Lee et. al.(PDF) Convolutional DBN's are proposed. Also the method is evaluated for image ...
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 ...