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Abstract: Factor analysis, principal component analysis (PCA), mixtures of Gaussian clusters, vector quantization (VQ), Kalman filter models and hidden Markov models can all be unified as variations of unsupervised learning under a single basic generative model. This is achieved by collecting together disparate observations and derivations made by many previous authors and introducing a new way of linking discrete and continuous state models using a simple nonlinearity. Through the use of other... (Update)
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BibTeX entry: (Update)
Roweis, S. and Ghahramani, Z. (1997). A unifying review of linear Gaussian models. Technical report, dept. http://citeseer.ist.psu.edu/roweis97unifying.html More
@techreport{ roweis97unifying,
author = "Sam Roweis and Zoubin Ghahramani",
title = "A Unifying Review of Linear {G}aussian Models",
address = "6 King's College Road, Toronto M5S 3H5, Canada",
year = "1997",
url = "citeseer.ist.psu.edu/roweis97unifying.html" }
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