Unsupervised learning

Machine learning and data mining
Problems
Classification Clustering Regression Anomaly detection Association rules Reinforcement learning Structured prediction Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank Grammar induction
Supervised learning (classification • regression)
Decision trees Ensembles (Bagging, Boosting, Random forest) k-NN Linear regression Naive Bayes Neural networks Logistic regression Perceptron Relevance vector machine (RVM) Support vector machine (SVM)
Clustering
BIRCH Hierarchical k-means Expectation-maximization (EM) DBSCAN OPTICS Mean-shift
Dimensionality reduction
Factor analysis CCA ICA LDA NMF PCA t-SNE
Structured prediction
Graphical models (Bayes net, CRF, HMM)
Anomaly detection
k-NN Local outlier factor
Neural nets
Autoencoder Deep learning Multilayer perceptron RNN Restricted Boltzmann machine SOM Convolutional neural network
Theory
Bias-variance dilemma Computational learning theory Empirical risk minimization PAC learning Statistical learning VC theory
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In machine learning, the problem of unsupervised learning is that of trying to find hidden structure in unlabeled data. Since the examples given to the learner are unlabeled, there is no error or reward signal to evaluate a potential solution. This distinguishes unsupervised learning from supervised learning and reinforcement learning.

Unsupervised learning is closely related to the problem of density estimation in statistics.^[1] However unsupervised learning also encompasses many other techniques that seek to summarize and explain key features of the data. Many methods employed in unsupervised learning are based on data mining methods used to preprocess data.

Approaches to unsupervised learning include:

• clustering (e.g., k-means, mixture models, hierarchical clustering),^[2]

• Approaches for learning latent variable models such as
  • Expectation–maximization algorithm (EM)
  • Method of moments
  • Blind signal separation techniques, e.g.,
    • Principal component analysis,
    • Independent component analysis,
    • Non-negative matrix factorization,
    • Singular value decomposition.^[3]

Among vigilance parameter. ART networks are also used for many pattern recognition tasks, such as automatic target recognition and seismic signal processing. The first version of ART was "ART1", developed by Carpenter and Grossberg (1988).^[4]

Method of moments

One of the approaches in unsupervised learning is the method of moments. In the method of moments, the unknown parameters (of interest) in the model are related to the moments of one or more random variables, and thus, these unknown parameters can be estimated given the moments. The moments are usually estimated from samples in an empirical way. The basic moments are first and second order moments. For a random vector, the first order moment is the mean vector, and the second order moment is the covariance matrix (when the mean is zero). Higher order moments are usually represented using tensors which are the generalization of matrices to higher orders as multi-dimensional arrays.

In particular, the method of moments is shown to be effective in learning the parameters of latent variable models.^[5] Latent variable models are statistical models where in addition to the observed variables, a set of latent variables also exists which is not observed. A highly practical example of latent variable models in machine learning is the topic modeling which is a statistical model for generating the words (observed variables) in the document based on the topic (latent variable) of the document. In the topic modeling, the words in the document are generated according to different statistical parameters when the topic of the document is changed. It is shown that method of moments (tensor decomposition techniques) consistently recover the parameters of a large class of latent variable models under some assumptions.^[5]

Expectation–maximization algorithm (EM) is also one of the most practical methods for learning latent variable models. But, it can be stuck in local optima, and the global convergence of the algorithm to the true unknown parameters of the model is not guaranteed. While, for the method of moments, the global convergence is guaranteed under some conditions.^[5]

See also

• Cluster analysis
• Expectation–maximization algorithm
• Generative topographic map
• Multivariate analysis
• Radial basis function network

Notes

Further reading

• Bousquet, O.; von Luxburg, U.; Raetsch, G., eds. (2004). Advanced Lectures on Machine Learning. Springer-Verlag.  
•  
• Hastie, Trevor; Tibshirani, Robert (2009). The Elements of Statistical Learning: Data mining,Inference,and Prediction. New York: Springer. pp. 485–586.  
• ) neural networks (This book focuses on unsupervised learning in