A multiple cause mixture model for unsupervised learning
Neural Computation
Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic visualisation of high-dimensional binary data
Proceedings of the 1998 conference on Advances in neural information processing systems II
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
The Journal of Machine Learning Research
Noisy-OR Component Analysis and its Application to Link Analysis
The Journal of Machine Learning Research
Competition and multiple cause models
Neural Computation
Binary Matrix Factorization with Applications
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
The aspect Bernoulli model: multiple causes of presences and absences
Pattern Analysis & Applications
Uniqueness of Non-Negative Matrix Factorization
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Bayesian inference for nonnegative matrix factorisation models
Computational Intelligence and Neuroscience
Nonnegative Matrix and Tensor Factorizations: Applications to Exploratory Multi-way Data Analysis and Blind Source Separation
Mapping question items to skills with non-negative matrix factorization
ACM SIGKDD Explorations Newsletter
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A novel method called binNMF is introduced which aimed to extract hidden information from multivariate binary data sets. The method treats the problem in the spirit of blind source separation: The data are assumed to be generated by a superposition of several simultaneously acting sources or elementary causes which are not observable directly. The superposition process is based on a minimum of assumptions and reversed to identify the underlying sources. The method is motivated, developed, and demonstrated in the context of binary wafer test data which evolve during microchip fabrication.