unsupervised learning machine learning anddata mining problems classification clustering regression anomaly detection automl association rules reinforcement learning structured prediction feature engineering feature learning online learning semisupervised learning unsupervised learning learning to rank grammar induction supervised learning classification regression decision trees ensembles bagging boosting random forest knn linear regression naive bayes artificial neural networks logistic regression perceptron relevance vector machine rvm support vector machine svm clustering birch cure hierarchical kmeans expectationmaximization em dbscan optics meanshift dimensionality reduction factor analysis cca ica lda nmf pca tsne structured prediction graphical models bayes net conditional random field hidden markov anomaly detection knn local outlier factor artificial neural networks autoencoder deep learning multilayer perceptron rnn lstm gru restricted boltzmann machine som convolutional neural network unet reinforcement learning qlearning sarsa temporal difference td theory biasvariance dilemma computational learning theory empirical risk minimization occam learning pac learning statistical learning vc theory machinelearning venues nips icml ml jmlr arxivcslg glossary of artificial intelligence glossary of artificial intelligence related articles list of datasets for machinelearning research outline of machine learning machine learning portalvte unsupervised learning is a branch of machine learning that learns from test data that has not been labeled classified or categorized instead of responding to feedback unsupervised learning identifies commonalities in the data and reacts based on the presence or absence of such commonalities in each new piece of data alternatives include supervised learning and reinforcement learning a central application of unsupervised learning is in the field of density estimation in statistics though unsupervised learning encompasses many other domains involving summarizing and explaining data features approaches this article is in a list format that may be better presented using prose you can help by converting this article to prose if appropriate editing help is available september 2018 compared to supervised learning where training data is labeled with the appropriate classifications models using unsupervised learning must learn relationships between elements in a data set and classify the raw data without help this hunt for relationships can take many different algorithmic formsbut all models have the same goal of mimicking human logic by searching for indirect hidden structures patterns or features to analyze new data some of the most common algorithms used in unsupervised learning include clustering hierarchical clustering kmeans mixture models dbscan optics algorithm anomaly detection local outlier factor neural networks autoencoders deep belief nets hebbian learning generative adversarial networks selforganizing map approaches for learning latent variable models such as expectationmaximization algorithm em method of moments blind signal separation techniques principal component analysis independent component analysis nonnegative matrix factorization singular value decomposition neural networks the classical example of unsupervised learning in the study of neural networks is donald hebbs principle that is neurons that fire together wire together in hebbian learning the connection is reinforced irrespective of an error but is exclusively a function of the coincidence between action potentials between the two neurons a similar version that modifies synaptic weights takes into account the time between the action potentials spiketimingdependent plasticity or stdp hebbian learning has been hypothesized to underlie a range of cognitive functions such as pattern recognition and experiential learning among neural network models the selforganizing map som and adaptive resonance theory art are commonly used in unsupervised learning algorithms the som is a topographic organization in which nearby locations in the map represent inputs with similar properties the art model allows the number of clusters to vary with problem size and lets the user control the degree of similarity between members of the same clusters by means of a userdefined constant called the vigilance parameter art networks are used for many pattern recognition tasks such as automatic target recognition and seismic signal processing method of moments one of the statistical approaches for 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 empirically 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 multidimensional arrays in particular the method of moments is shown to be effective in learning the parameters of latent variable models 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 the expectationmaximization algorithm em is also one of the most practical methods for learning latent variable models however it can get stuck in local optima and it is not guaranteed that the algorithm will converge to the true unknown parameters of the model in contrast for the method of moments the global convergence is guaranteed under some conditions automated machine learning cluster analysis anomaly detection expectationmaximization algorithm generative topographic map metalearning computer science multivariate analysis radial basis function network hebbian theory notes bousquet o von luxburg u raetsch g eds 2004 advanced lectures on machine learning springerverlag isbn9783540231226 duda richard o hart peter e stork david g 2001 unsupervised learning and clustering pattern classification 2nd ed wiley isbn0471056693 hastie trevor tibshirani robert 2009 the elements of statistical learning data mining inference and prediction new york springer pp485586 doi101007978038784858714 isbn9780387848570 hinton geoffrey sejnowski terrence j eds 1999 unsupervised learning foundations of neural computation mit press isbn026258168x this book focuses on unsupervised learning in neural networks 