A multiple cause mixture model for unsupervised learning
Neural Computation
An introduction to variational methods for graphical models
Learning in graphical models
Probabilistic visualisation of high-dimensional binary data
Proceedings of the 1998 conference on Advances in neural information processing systems II
The Journal of Machine Learning Research
Applying discrete PCA in data analysis
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Bayesian Feature and Model Selection for Gaussian Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Noisy-OR Component Analysis and its Application to Link Analysis
The Journal of Machine Learning Research
Deconvolutive clustering of markov states
ECML'06 Proceedings of the 17th European conference on Machine Learning
ICA-Based binary feature construction
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Blind Deconvolution of Multi-Input Single-Output Systems With Binary Sources
IEEE Transactions on Signal Processing
Multi-assignment clustering for Boolean data
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Multi-assignment clustering for boolean data
The Journal of Machine Learning Research
Role Mining with Probabilistic Models
ACM Transactions on Information and System Security (TISSEC)
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Presence-absence (0-1) observations are special in that often the absence of evidence is not evidence of absence. Here we develop an independent factor model, which has the unique capability to isolate the former as an independent discrete binary noise factor. This representation then forms the basis of inferring missed presences by means of denoising. This is achieved in a probabilistic formalism, employing independent beta latent source densities and a Bernoulli data likelihood model. Variational approximations are employed to make the inferences tractable. We relate our model to existing models of 0-1 data, demonstrating its advantages for the problem considered, and we present applications in several problem domains, including social network analysis and DNA fingerprint analysis.