Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Bayesian independent component analysis: Variational methods and non-negative decompositions
Digital Signal Processing
Projected Gradient Methods for Nonnegative Matrix Factorization
Neural Computation
Nonnegative matrix factorization with Gaussian process priors
Computational Intelligence and Neuroscience - Advances in Nonnegative Matrix and Tensor Factorization
Csiszár’s divergences for non-negative matrix factorization: family of new algorithms
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Nonnegative matrix factor 2-d deconvolution for blind single channel source separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Nonnegative matrix factorization with Gaussian process priors
Computational Intelligence and Neuroscience - Advances in Nonnegative Matrix and Tensor Factorization
Bayesian Non-negative Matrix Factorization
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Function factorization using warped Gaussian processes
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Bayesian inference for nonnegative matrix factorisation models
Computational Intelligence and Neuroscience
IEEE Transactions on Audio, Speech, and Language Processing
Using population based algorithms for initializing nonnegative matrix factorization
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Unmixing of Hyperspectral Images using Bayesian Non-negative Matrix Factorization with Volume Prior
Journal of Signal Processing Systems
Correlated Noise: How it Breaks NMF, and What to Do About it
Journal of Signal Processing Systems
Digital Signal Processing
A convergent algorithm for orthogonal nonnegative matrix factorization
Journal of Computational and Applied Mathematics
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We present a general method for including prior knowledge in a nonnegative matrix factorization (NMF), based on Gaussian process priors. We assume that the nonnegative factors in the NMF are linked by a strictly increasing function to an underlying Gaussian process specified by its covariance function. This allows us to find NMF decompositions that agree with our prior knowledge of the distribution of the factors, such as sparseness, smoothness, and symmetries. The method is demonstrated with an example from chemical shift brain imaging.