The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
A Unifying Review of Linear Gaussian Models
Abstract
Factor analysis, principal component analysis, mixtures of gaussian clusters, vector quantization, 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 nonlinearities, we show how independent component analysis is also a variation of the same basic generative model. We show that factor analysis and mixtures of gaussians can be implemented in autoencoder neural networks and learned using squared error plus the same regularization term. We introduce a new model for static data, known as sensible principal component analysis, as well as a novel concept of spatially adaptive observation noise. We also review some of the literature involving global and local mixtures of the basic models and provide pseudocode for inference and learning for all the basic models.
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Cites
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Cited by
The free-energy principle: a unified brain theory?
Friston, K. (2010) · Nature Reviews Neuroscience · doi:10.1038/nrn2787 · Cited by 8,120
Independent Component Analysis: Principles and Practice
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Mixtures of Probabilistic Principal Component Analyzers
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(2003) · Cited by 1,680
Multimodal fusion for multimedia analysis: a survey
Atrey, P. K., Hossain, M. A., El Saddik, A., & Kankanhalli, M. S. (2010) · Multimedia Systems · doi:10.1007/s00530-010-0182-0 · Cited by 1,279
Metadata
- Authors
- Roweis, S., & Ghahramani, Z.
- Venue
- Neural Computation
- Publisher
- MIT Press
- Published
- 1999-02-01
- Volume
- 11
- Issue
- 2
- Pages
- 305-345
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2103139809
- Cited by
- 869
- References
- 62
- FWCI
- 32.15
- Type
- journal article
- Language
- en
- Open access
- closed
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