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Inference of Transcriptional Regulatory Networks (TRNs) provides insight into the mechanisms driving biological systems, especially mammalian development and disease. Many techniques have been developed for TRN estimation from indirect biochemical measurements. Although successful when initially tested in model organisms, these regulatory models often fail when applied to data from multicellular organisms...
Probabilistic models of audio spectrograms used in audio source separation often rely on Poisson or multinomial noise models corresponding to the generalized Kullback-Leibler (GKL) divergence popular in methods using Nonnegative Matrix Factorization (NMF). This noise model works well in practice, but it is difficult to justify since these distributions are technically only applicable to discrete counts...
In this paper we develop a probabilistic interpretation and a full Bayesian inference for non-negative matrix deconvolution (NMFD) model. Our ultimate goal is unsupervised extraction of multiple sound objects from a single channel auditory scene. The proposed method facilitates automatic model selection and determination of the sparsity criteria. Our approach retains attractive features of standard...
In this paper, a new approach of synthetic aperture radar (SAR) image target recognition based on non-negative matrix factorization (NMF) feature extraction and Bayesian decision fusion is presented for recognizing ground vehicles in MSTAR database. First, feature vectors are extracted from image chips by NMF algorithm. Support vector machine (SVM) is used to classify the feature vectors. After multiple...
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