Through comparing the concepts of Bayesian factor analysis and Bayesian sparse representation, we can find that the main difference between them is that the former applies a sparsity-inducing distribution over the factor loading matrix, while the later uses a sparsity-inducing distribution on the factor score vector.
The coefficient vector should follows a sparsity-inducing distribution.
Sparse Bayesian factor analysis model imposes a sparsity-inducing distribution over the factor loading matrix instead of Gaussian distribution.
The parameter b is used to control the sparsity of the distribution, for example, when b=1 the distribution is Laplacian, b=2 the distribution is Gaussian, and b→+∞ the distribution is uniform.
We analyze the intrinsic sparsity and imbalanced distribution of speaker frames, those redundant Gaussian components, are discarded in the i-vector extraction phase so as to compensate the phonetic variability.
To create this framework, an experimental design applies several CF configurations, which are characterized by different data-reduction techniques, CF methods, and similarity measures, to binary purchase data sets with distinct input data characteristics, i.e., sparsity level, purchase distribution, and item user ratio.
We have shown here that the sparsity and uneven distribution of CG sites due to deamination biases averaging-based studies of 5hmC that have attempted to determine 5hmC rates in close proximity to exon-intron junctions.
If the set of measurements are sufficiently independent and distributed given the sparsity of the bioluminescence distribution being imaged, then there is a unique solution with maximum sparsity that matches the measurements, and that solution is the desired image.
The choice of these so-called hyper-parameters determines the sparsity of the distributions and thus the variability in likelihood with which words will be assigned to topics and topics to documents.
